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imthekaiser

imthekaiser

Verified

@imthekaiser · Member since 2026

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Verified 1w ago Mainly vllm 200 approved runs

Submissions

1,029

200 approved public runs

Models

21

unique models

Avg output

423tok/s

Best 2,566 tok/s

Avg prefill

466tok/s

Best 4,675 tok/s

Avg total

895tok/s

Best 5,587 tok/s

Avg TTFT

3,647ms

Best 63.5 ms

Hardware

1

distinct setup

Matrix runs

36

speed test matrices

Matrix runs

Matrix runs 36

A matrix run measures prompt reading (prefill) and generation (decode) speed in one go, across several context lengths and numbers of simultaneous requests. Showing the newest 10.
  • Qwen3.8-Flash-Next-W4A16Intel Arc Pro B65 32GB ×4 · vllm 0.30.0 · INT4_W4A166 contexts × 3 concurrency levels
    Peak decode
    242.6 tok/s
    Peak prefill
    3,961 tok/s
  • Qwen3.8-Flash-Next-W4A16Intel Arc Pro B65 32GB ×4 · vllm 0.30.0 · INT4_W4A166 contexts × 3 concurrency levels
    Peak decode
    158.3 tok/s
    Peak prefill
    3,856 tok/s
  • Qwen3.8-Flash-Next-W4A16Intel Arc Pro B65 32GB ×4 · vllm 0.30.0 · INT4_W4A166 contexts × 3 concurrency levels
    Peak decode
    246.3 tok/s
    Peak prefill
    5,206 tok/s
  • Qwen3.8-Flash-Next-W4A16Intel Arc Pro B65 32GB ×4 · vllm 0.30.0 · INT4_W4A166 contexts × 3 concurrency levels
    Peak decode
    158.6 tok/s
    Peak prefill
    5,306 tok/s
  • gemma-4-31B-itIntel Arc Pro B65 32GB · vllm 0.31.0+xpu.llmscaler · INT41 context × 5 concurrency levels
    Peak decode
    207.3 tok/s
    Peak prefill
    —
  • gemma-4-26B-A4B-itIntel Arc Pro B65 32GB · vllm 0.32.0 · FP81 context × 5 concurrency levels
    Peak decode
    425.3 tok/s
    Peak prefill
    —
  • gemma-4-12B-itIntel Arc Pro B65 32GB · vllm 0.32.0 · BF161 context × 5 concurrency levels
    Peak decode
    262.4 tok/s
    Peak prefill
    —
  • gemma-4-31B-itIntel Arc Pro B65 32GB ×4 · vllm 0.32.0 · BF163 contexts × 5 concurrency levels
    Peak decode
    284.5 tok/s
    Peak prefill
    592.0 tok/s
  • gemma-4-12B-itIntel Arc Pro B65 32GB ×4 · vllm 0.32.0 · BF164 contexts × 5 concurrency levels
    Peak decode
    648.0 tok/s
    Peak prefill
    1,407 tok/s
  • gemma-4-31B-itIntel Arc Pro B65 32GB ×4 · vllm 0.31.0+xpu.llmscaler · FP84 contexts × 5 concurrency levels
    Peak decode
    396.8 tok/s
    Peak prefill
    1,123 tok/s
See all 36 matrix runs
Personal bests

Personal bests21

Fastest approved result for each model.

4B15 runs
Output2,566 tok/s
Prefill76.2 tok/s
TTFT4,026 ms
Depth0k
RTX A6000 48GB

vllm · BF16 · today

3B MoE8 runs
Output2,559 tok/s
Prefill61.0 tok/s
TTFT4,880 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

3B MoE6 runs
Output2,490 tok/s
Prefill60.9 tok/s
TTFT4,853 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

9B17 runs
Output1,746 tok/s
Prefill44.6 tok/s
TTFT6,884 ms
Depth0k
RTX A6000 48GB

vllm · BF16 · today

3B MoE11 runs
Output1,440 tok/s
Prefill196 tok/s
TTFT1,559 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

12B9 runs
Output990 tok/s
Prefill31.8 tok/s
TTFT9,546 ms
Depth0k
RTX A6000 48GB

vllm · BF16 · today

32B9 runs
Output617 tok/s
Prefill24.8 tok/s
TTFT12,896 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

32B8 runs
Output609 tok/s
Prefill21.6 tok/s
TTFT13,719 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

27B13 runs
Output490 tok/s
Prefill37.3 tok/s
TTFT8,185 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

80B MoE16 runs
Output406 tok/s
Prefill127 tok/s
TTFT2,335 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q3_K_XL · today

70B5 runs
Output261 tok/s
Prefill40.3 tok/s
TTFT7,796 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

70B6 runs
Output259 tok/s
Prefill33.3 tok/s
TTFT9,210 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

10B MoE25 runs
Output224 tok/s
Prefill58.8 tok/s
TTFT5,156 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

72B6 runs
Output178 tok/s
Prefill68.7 tok/s
TTFT4,960 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

180B14 runs
Output170 tok/s
Prefill52.6 tok/s
TTFT6,561 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

17B MoE12 runs
Output165 tok/s
Prefill137 tok/s
TTFT2,007 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q2_K · today

120B MoE1 run
Output82.5 tok/s
Prefill30.0 tok/s
TTFT11,107 ms
Depth0k
RTX A6000 48GB

llama.cpp · MXFP4 · today

304B MoE8 runs
Output40.7 tok/s
Prefill26.0 tok/s
TTFT10,509 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

311B MoE4 runs
Output25.2 tok/s
Prefill56.6 tok/s
TTFT5,212 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q2_K · today

108B MoE4 runs
Output16.4 tok/s
Prefill95.1 tok/s
TTFT2,945 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q8_0 · today

321B3 runs
Output16.0 tok/s
Prefill11.7 tok/s
TTFT24,401 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

Engines used

Engines used2

Runtime mix and top quantizations.

vllm113BF16 · AWQ · FP8
llama.cpp87IQ4_XS · Q8_0 · UD-Q3_K_XL
All runs

All runs200

Full approved speed test history · page 1 of 8.

73.8

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · IQ4_XS

TTFT

6,929 ms

Context

2k · today

Show all run details

Model

meta-llama/Llama-4-Scout-17B-16E-Instruct

Display name

Llama-4-Scout-17B-16E-Instruct

Base model

Llama-4-Scout-17B-16E

Revision

91d32823a1855f7ca12919dffa86701254271eb9

Family

Llama

Parameters

109B

Active params

17B

MoE

yes

Output

73.8 tok/s

Prefill

39.8 tok/s

Total

136 tok/s

TTFT

6,929 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

276

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

IQ4_XS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-l4s-iq4xs-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Llama-4-Scout-more/meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS/meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-l4s-iq4xs-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 73.8 tok/s is the aggregate decode rate across all 16 streams, about 4.6 tok/s per stream, and the 6929 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 59 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47370 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF @ 91d32823a1855f7ca12919dffa86701254271eb9, file meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 80.5, 73.8, 73.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:40 PM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

17.4

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q8_0

TTFT

5,749 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

—

Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

—

MoE

yes

Output

17.4 tok/s

Prefill

51.3 tok/s

Total

36.7 tok/s

TTFT

5,749 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q8_0

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-qcn-q8-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3-Coder-Next-Q8/Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q8-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 17.4 tok/s is the aggregate decode rate across all 16 streams, about 1.1 tok/s per stream, and the 5749 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 84 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47564 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-Q8_0-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 17.4, 155.2, 30 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:30 PM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

406

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q3_K_XL

TTFT

2,335 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

—

Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

—

MoE

yes

Output

406 tok/s

Prefill

127 tok/s

Total

711 tok/s

TTFT

2,335 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-qcn-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3-Coder-Next-small/Qwen3-Coder-Next-UD-Q3_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 406.1 tok/s is the aggregate decode rate across all 16 streams, about 25.4 tok/s per stream, and the 2335 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-UD-Q3_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 421.2, 406.1, 417.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:30 PM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

265

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q3_K_XL

TTFT

1,654 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

—

Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

—

MoE

yes

Output

265 tok/s

Prefill

179 tok/s

Total

500 tok/s

TTFT

1,654 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-qcn-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3-Coder-Next-small/Qwen3-Coder-Next-UD-Q3_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 265 tok/s is the aggregate decode rate across all 12 streams, about 22.1 tok/s per stream, and the 1654 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-UD-Q3_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 265, 388.9, 261 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:30 PM

Last edited

—

54.1

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · IQ4_XS

TTFT

4,772 ms

Context

2k · today

Show all run details

Model

meta-llama/Llama-4-Scout-17B-16E-Instruct

Display name

Llama-4-Scout-17B-16E-Instruct

Base model

Llama-4-Scout-17B-16E

Revision

91d32823a1855f7ca12919dffa86701254271eb9

Family

Llama

Parameters

109B

Active params

17B

MoE

yes

Output

54.1 tok/s

Prefill

57.8 tok/s

Total

104 tok/s

TTFT

4,772 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

276

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

IQ4_XS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-l4s-iq4xs-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Llama-4-Scout-more/meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS/meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-l4s-iq4xs-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 54.1 tok/s is the aggregate decode rate across all 12 streams, about 4.5 tok/s per stream, and the 4772 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 59 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47370 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF @ 91d32823a1855f7ca12919dffa86701254271eb9, file meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 54.1, 64, 46.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 27, 2026, 12:30 PM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

27.4

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q8_0

TTFT

5,118 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

—

Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

—

MoE

yes

Output

27.4 tok/s

Prefill

58.1 tok/s

Total

56.5 tok/s

TTFT

5,118 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q8_0

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-qcn-q8-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3-Coder-Next-Q8/Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q8-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 27.4 tok/s is the aggregate decode rate across all 12 streams, about 2.3 tok/s per stream, and the 5118 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 84 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47564 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-Q8_0-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 106.8, 35.8, 27.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:20 PM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

37.8

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · MXFP4_MOE

TTFT

9,709 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

37.8 tok/s

Prefill

31.3 tok/s

Total

76.0 tok/s

TTFT

9,709 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 37.8 tok/s is the aggregate decode rate across all 16 streams, about 2.4 tok/s per stream, and the 9709 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47554 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 31, 37.8, 51.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:10 PM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

76.1

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q4_K_XL

TTFT

10,869 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

76.1 tok/s

Prefill

28.0 tok/s

Total

138 tok/s

TTFT

10,869 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q4kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q4/UD-Q4_K_XL/Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q4kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 76.1 tok/s is the aggregate decode rate across all 16 streams, about 4.8 tok/s per stream, and the 10869 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 77 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47488 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 79.8, 77.7, 76.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:00 PM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

37.3

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · MXFP4_MOE

TTFT

6,652 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

37.3 tok/s

Prefill

45.5 tok/s

Total

75.4 tok/s

TTFT

6,652 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 37.3 tok/s is the aggregate decode rate across all 12 streams, about 3.1 tok/s per stream, and the 6652 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47554 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 37.3, 38.9, 19.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:00 PM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

54.4

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q4_K_XL

TTFT

12,086 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

54.4 tok/s

Prefill

25.2 tok/s

Total

98.1 tok/s

TTFT

12,086 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q4kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q4/UD-Q4_K_XL/Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q4kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 54.4 tok/s is the aggregate decode rate across all 12 streams, about 4.5 tok/s per stream, and the 12086 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 77 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47488 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 48.1, 54.4, 34.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:50 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

97.7

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q3_K_XL

TTFT

9,099 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

97.7 tok/s

Prefill

33.4 tok/s

Total

176 tok/s

TTFT

9,099 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q3/UD-Q3_K_XL/Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 97.7 tok/s is the aggregate decode rate across all 16 streams, about 6.1 tok/s per stream, and the 9099 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 56 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47396 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 110.5, 97.7, 81.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:49 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

67.5

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q3_K_XL

TTFT

4,948 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

67.5 tok/s

Prefill

61.4 tok/s

Total

133 tok/s

TTFT

4,948 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q3/UD-Q3_K_XL/Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 67.5 tok/s is the aggregate decode rate across all 12 streams, about 5.6 tok/s per stream, and the 4948 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 56 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47396 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 73.6, 105.9, 67.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:39 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

224

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

5,156 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

224 tok/s

Prefill

58.8 tok/s

Total

383 tok/s

TTFT

5,156 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q2_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q2kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q2/Qwen3.5-122B-A10B-UD-Q2_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q2kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 224.2 tok/s is the aggregate decode rate across all 16 streams, about 14.0 tok/s per stream, and the 5156 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q2_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 224.2, 225.6, 226 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:39 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

159

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

3,727 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

159 tok/s

Prefill

81.3 tok/s

Total

292 tok/s

TTFT

3,727 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q2_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q2kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q2/Qwen3.5-122B-A10B-UD-Q2_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q2kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 159.4 tok/s is the aggregate decode rate across all 12 streams, about 13.3 tok/s per stream, and the 3727 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q2_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 161.9, 206.1, 159.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:39 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

37.6

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q4_K_XL

TTFT

19,434 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

37.6 tok/s

Prefill

17.8 tok/s

Total

75.1 tok/s

TTFT

19,434 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-q4kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next-Q4/UD-Q4_K_XL/Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-q4kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 37.6 tok/s is the aggregate decode rate across all 16 streams, about 2.4 tok/s per stream, and the 19434 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 111 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47330 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 37.6, 55.9, 38.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:39 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

76.0

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q4_K_XL

TTFT

13,078 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

76.0 tok/s

Prefill

26.5 tok/s

Total

135 tok/s

TTFT

13,078 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-q4kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next-Q4/UD-Q4_K_XL/Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-q4kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 76 tok/s is the aggregate decode rate across all 12 streams, about 6.3 tok/s per stream, and the 13078 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 111 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47330 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 49.4, 76, 85.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:29 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

111

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q3_K_XL

TTFT

6,262 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

111 tok/s

Prefill

55.2 tok/s

Total

223 tok/s

TTFT

6,262 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next/UD-Q3_K_XL/Qwen3.8-Flash-Next-UD-Q3_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 111 tok/s is the aggregate decode rate across all 16 streams, about 6.9 tok/s per stream, and the 6262 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 89 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47554 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-Q3_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 112.8, 111, 110.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:19 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

101

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q3_K_XL

TTFT

3,916 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

101 tok/s

Prefill

88.6 tok/s

Total

211 tok/s

TTFT

3,916 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next/UD-Q3_K_XL/Qwen3.8-Flash-Next-UD-Q3_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 101.3 tok/s is the aggregate decode rate across all 12 streams, about 8.4 tok/s per stream, and the 3916 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 89 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47554 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-Q3_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 101.3, 104.8, 105.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:19 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

148

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ3_XXS

TTFT

8,711 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

148 tok/s

Prefill

39.8 tok/s

Total

265 tok/s

TTFT

8,711 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ3_XXS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-iq3xxs-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next-IQ3/UD-IQ3_XXS/Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-iq3xxs-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 148.2 tok/s is the aggregate decode rate across all 16 streams, about 9.3 tok/s per stream, and the 8711 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 81 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47608 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 161.1, 156.7, 148.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:19 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

131

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ3_XXS

TTFT

4,871 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

131 tok/s

Prefill

71.2 tok/s

Total

255 tok/s

TTFT

4,871 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ3_XXS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-iq3xxs-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next-IQ3/UD-IQ3_XXS/Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-iq3xxs-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 130.7 tok/s is the aggregate decode rate across all 12 streams, about 10.9 tok/s per stream, and the 4871 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 81 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47608 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 132.3, 130.7, 111.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:19 AM

Last edited

—

12.5

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q2_K

TTFT

32,129 ms

Context

2k · today

Show all run details

Model

XiaomiMiMo/MiMo-V2.6-Flash-RL

Display name

MiMo-V2.6-Flash-RL

Base model

—

Revision

f1ef9bfdc3706af3f807752765e52b885e113aae

Family

—

Parameters

311B

Active params

—

MoE

yes

Output

12.5 tok/s

Prefill

9.2 tok/s

Total

24.5 tok/s

TTFT

32,129 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q2_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-mimo26f-q2k-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/MiMo-V2.6-Flash/MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mimo26f-q2k-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 12.5 tok/s is the aggregate decode rate across all 16 streams, about 0.8 tok/s per stream, and the 32129 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 126 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 46694 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF ggml-org/MiMo-V2.6-Flash-RL-GGUF @ f1ef9bfdc3706af3f807752765e52b885e113aae, file MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 25, 17.6, 12.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:09 AM

Last edited

—

16.0

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

24,401 ms

Context

2k · today

Show all run details

Model

zai-org/GLM-5.3-Flash

Display name

GLM-5.3-Flash

Base model

—

Revision

a38483c8cd5df544f53d70fb281afe97369d5ab6

Family

—

Parameters

321B

Active params

—

MoE

no

Output

16.0 tok/s

Prefill

11.7 tok/s

Total

30.9 tok/s

TTFT

24,401 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

285

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q2_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-glm53f-q2kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/GLM-5.3-Flash/UD-Q2_K_XL/GLM-5.3-Flash-UD-Q2_K_XL-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-glm53f-q2kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 16 tok/s is the aggregate decode rate across all 16 streams, about 1.0 tok/s per stream, and the 24401 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 108 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47624 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/GLM-5.3-Flash-GGUF @ a38483c8cd5df544f53d70fb281afe97369d5ab6, file GLM-5.3-Flash-UD-Q2_K_XL-00001-of-00004.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 19.5, 12.7, 16 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 27, 2026, 10:49 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

40.7

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

10,509 ms

Context

2k · today

Show all run details

Model

deepseek-ai/DeepSeek-V4-Flash-0731

Display name

DeepSeek-V4-Flash-0731

Base model

—

Revision

fbbb5b93fb787c21338159b0af3318bb3f4d9768

Family

Deepseek

Parameters

304B

Active params

—

MoE

yes

Output

40.7 tok/s

Prefill

26.0 tok/s

Total

76.2 tok/s

TTFT

10,509 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q2_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 40.7 tok/s is the aggregate decode rate across all 16 streams, about 2.5 tok/s per stream, and the 10509 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47256 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 40.7, 40.5, 40 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 10:29 AM

Last edited

—

64.8

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q4_K_M

TTFT

8,440 ms

Context

2k · today

Show all run details

Model

meta-llama/Llama-4-Scout-17B-16E-Instruct

Display name

Llama-4-Scout-17B-16E-Instruct

Base model

Llama-4-Scout-17B-16E

Revision

91d32823a1855f7ca12919dffa86701254271eb9

Family

Llama

Parameters

109B

Active params

17B

MoE

yes

Output

64.8 tok/s

Prefill

32.7 tok/s

Total

119 tok/s

TTFT

8,440 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

276

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q4_K_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-l4s-q4km-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Llama-4-Scout/meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M/meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-l4s-q4km-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 64.8 tok/s is the aggregate decode rate across all 16 streams, about 4.0 tok/s per stream, and the 8440 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 67 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47410 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF @ 91d32823a1855f7ca12919dffa86701254271eb9, file meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 67, 64.8, 42.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 10:19 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

303

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q4_K_XL

TTFT

2,861 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

—

Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

—

MoE

yes

Output

303 tok/s

Prefill

104 tok/s

Total

540 tok/s

TTFT

2,861 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-qcn-q4kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3-Coder-Next/Qwen3-Coder-Next-UD-Q4_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q4kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 303 tok/s is the aggregate decode rate across all 16 streams, about 18.9 tok/s per stream, and the 2861 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 49 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47442 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-UD-Q4_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 301.6, 304.4, 303 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 27, 2026, 10:09 AM

Last edited

—

ModelHardwareEnginetok/s outprefilltok/s totalTTFTDepthShare
Llama-4-Scout-17B-16E-Instruct

17B MoE · Llama

RTX A6000 48GB
llama.cppIQ4_XS73.839.81366,929 ms0k
Show all details for Llama-4-Scout-17B-16E-Instruct

Model

meta-llama/Llama-4-Scout-17B-16E-Instruct

Display name

Llama-4-Scout-17B-16E-Instruct

Base model

Llama-4-Scout-17B-16E

Revision

91d32823a1855f7ca12919dffa86701254271eb9

Family

Llama

Parameters

109B

Active params

17B

MoE

yes

Output

73.8 tok/s

Prefill

39.8 tok/s

Total

136 tok/s

TTFT

6,929 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

276

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

IQ4_XS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-l4s-iq4xs-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Llama-4-Scout-more/meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS/meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-l4s-iq4xs-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 73.8 tok/s is the aggregate decode rate across all 16 streams, about 4.6 tok/s per stream, and the 6929 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 59 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47370 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF @ 91d32823a1855f7ca12919dffa86701254271eb9, file meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 80.5, 73.8, 73.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:40 PM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppQ8_017.451.336.75,749 ms0k
Show all details for Qwen3-Coder-Next

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

—

Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

—

MoE

yes

Output

17.4 tok/s

Prefill

51.3 tok/s

Total

36.7 tok/s

TTFT

5,749 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q8_0

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-qcn-q8-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3-Coder-Next-Q8/Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q8-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 17.4 tok/s is the aggregate decode rate across all 16 streams, about 1.1 tok/s per stream, and the 5749 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 84 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47564 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-Q8_0-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 17.4, 155.2, 30 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:30 PM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q3_K_XL4061277112,335 ms0k
Show all details for Qwen3-Coder-Next

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

—

Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

—

MoE

yes

Output

406 tok/s

Prefill

127 tok/s

Total

711 tok/s

TTFT

2,335 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-qcn-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3-Coder-Next-small/Qwen3-Coder-Next-UD-Q3_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 406.1 tok/s is the aggregate decode rate across all 16 streams, about 25.4 tok/s per stream, and the 2335 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-UD-Q3_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 421.2, 406.1, 417.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:30 PM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q3_K_XL2651795001,654 ms0k
Show all details for Qwen3-Coder-Next

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

—

Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

—

MoE

yes

Output

265 tok/s

Prefill

179 tok/s

Total

500 tok/s

TTFT

1,654 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-qcn-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3-Coder-Next-small/Qwen3-Coder-Next-UD-Q3_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 265 tok/s is the aggregate decode rate across all 12 streams, about 22.1 tok/s per stream, and the 1654 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-UD-Q3_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 265, 388.9, 261 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:30 PM

Last edited

—

Llama-4-Scout-17B-16E-Instruct

17B MoE · Llama

RTX A6000 48GB
llama.cppIQ4_XS54.157.81044,772 ms0k
Show all details for Llama-4-Scout-17B-16E-Instruct

Model

meta-llama/Llama-4-Scout-17B-16E-Instruct

Display name

Llama-4-Scout-17B-16E-Instruct

Base model

Llama-4-Scout-17B-16E

Revision

91d32823a1855f7ca12919dffa86701254271eb9

Family

Llama

Parameters

109B

Active params

17B

MoE

yes

Output

54.1 tok/s

Prefill

57.8 tok/s

Total

104 tok/s

TTFT

4,772 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

276

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

IQ4_XS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-l4s-iq4xs-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Llama-4-Scout-more/meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS/meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-l4s-iq4xs-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 54.1 tok/s is the aggregate decode rate across all 12 streams, about 4.5 tok/s per stream, and the 4772 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 59 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47370 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF @ 91d32823a1855f7ca12919dffa86701254271eb9, file meta-llama_Llama-4-Scout-17B-16E-Instruct-IQ4_XS-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 54.1, 64, 46.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 27, 2026, 12:30 PM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppQ8_027.458.156.55,118 ms0k
Show all details for Qwen3-Coder-Next

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

—

Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

—

MoE

yes

Output

27.4 tok/s

Prefill

58.1 tok/s

Total

56.5 tok/s

TTFT

5,118 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q8_0

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-qcn-q8-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3-Coder-Next-Q8/Q8_0/Qwen3-Coder-Next-Q8_0-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q8-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 27.4 tok/s is the aggregate decode rate across all 12 streams, about 2.3 tok/s per stream, and the 5118 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 84 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47564 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-Q8_0-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 106.8, 35.8, 27.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:20 PM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppMXFP4_MOE37.831.376.09,709 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

37.8 tok/s

Prefill

31.3 tok/s

Total

76.0 tok/s

TTFT

9,709 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 37.8 tok/s is the aggregate decode rate across all 16 streams, about 2.4 tok/s per stream, and the 9709 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47554 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 31, 37.8, 51.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:10 PM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q4_K_XL76.128.013810,869 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

76.1 tok/s

Prefill

28.0 tok/s

Total

138 tok/s

TTFT

10,869 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q4kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q4/UD-Q4_K_XL/Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q4kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 76.1 tok/s is the aggregate decode rate across all 16 streams, about 4.8 tok/s per stream, and the 10869 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 77 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47488 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 79.8, 77.7, 76.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:00 PM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppMXFP4_MOE37.345.575.46,652 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

37.3 tok/s

Prefill

45.5 tok/s

Total

75.4 tok/s

TTFT

6,652 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

MXFP4_MOE

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-mxfp4-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-MXFP4/MXFP4_MOE/Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-mxfp4-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 37.3 tok/s is the aggregate decode rate across all 12 streams, about 3.1 tok/s per stream, and the 6652 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 74 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47554 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-MXFP4_MOE-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 37.3, 38.9, 19.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 12:00 PM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q4_K_XL54.425.298.112,086 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

54.4 tok/s

Prefill

25.2 tok/s

Total

98.1 tok/s

TTFT

12,086 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q4kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q4/UD-Q4_K_XL/Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q4kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 54.4 tok/s is the aggregate decode rate across all 12 streams, about 4.5 tok/s per stream, and the 12086 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 77 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47488 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q4_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 48.1, 54.4, 34.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:50 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q3_K_XL97.733.41769,099 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

97.7 tok/s

Prefill

33.4 tok/s

Total

176 tok/s

TTFT

9,099 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q3/UD-Q3_K_XL/Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 97.7 tok/s is the aggregate decode rate across all 16 streams, about 6.1 tok/s per stream, and the 9099 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 56 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47396 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 110.5, 97.7, 81.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:49 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q3_K_XL67.561.41334,948 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

67.5 tok/s

Prefill

61.4 tok/s

Total

133 tok/s

TTFT

4,948 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q3/UD-Q3_K_XL/Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 67.5 tok/s is the aggregate decode rate across all 12 streams, about 5.6 tok/s per stream, and the 4948 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 56 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47396 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q3_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 73.6, 105.9, 67.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:39 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q2_K_XL22458.83835,156 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

224 tok/s

Prefill

58.8 tok/s

Total

383 tok/s

TTFT

5,156 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q2_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q2kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q2/Qwen3.5-122B-A10B-UD-Q2_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q2kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 224.2 tok/s is the aggregate decode rate across all 16 streams, about 14.0 tok/s per stream, and the 5156 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q2_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 224.2, 225.6, 226 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:39 AM

Last edited

—

Qwen3.5-122B-A10B

10B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q2_K_XL15981.32923,727 ms0k
Show all details for Qwen3.5-122B-A10B

Model

Qwen/Qwen3.5-122B-A10B

Display name

Qwen3.5-122B-A10B

Base model

—

Revision

51eab4d59d53f573fb9206cb3ce613f1d0aa392b

Family

Qwen

Parameters

122B

Active params

10B

MoE

yes

Output

159 tok/s

Prefill

81.3 tok/s

Total

292 tok/s

TTFT

3,727 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q2_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q122-q2kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.5-122B-Q2/Qwen3.5-122B-A10B-UD-Q2_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-q122-q2kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 159.4 tok/s is the aggregate decode rate across all 12 streams, about 13.3 tok/s per stream, and the 3727 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.5-122B-A10B-GGUF @ 51eab4d59d53f573fb9206cb3ce613f1d0aa392b, file Qwen3.5-122B-A10B-UD-Q2_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 161.9, 206.1, 159.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:39 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

RTX A6000 48GB
llama.cppUD-Q4_K_XL37.617.875.119,434 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

37.6 tok/s

Prefill

17.8 tok/s

Total

75.1 tok/s

TTFT

19,434 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-q4kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next-Q4/UD-Q4_K_XL/Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-q4kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 37.6 tok/s is the aggregate decode rate across all 16 streams, about 2.4 tok/s per stream, and the 19434 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 111 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47330 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 37.6, 55.9, 38.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:39 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

RTX A6000 48GB
llama.cppUD-Q4_K_XL76.026.513513,078 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

76.0 tok/s

Prefill

26.5 tok/s

Total

135 tok/s

TTFT

13,078 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-q4kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next-Q4/UD-Q4_K_XL/Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-q4kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 76 tok/s is the aggregate decode rate across all 12 streams, about 6.3 tok/s per stream, and the 13078 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 111 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47330 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-Q4_K_XL-00001-of-00004.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 49.4, 76, 85.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:29 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

RTX A6000 48GB
llama.cppUD-Q3_K_XL11155.22236,262 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

111 tok/s

Prefill

55.2 tok/s

Total

223 tok/s

TTFT

6,262 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next/UD-Q3_K_XL/Qwen3.8-Flash-Next-UD-Q3_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 111 tok/s is the aggregate decode rate across all 16 streams, about 6.9 tok/s per stream, and the 6262 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 89 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47554 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-Q3_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 112.8, 111, 110.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:19 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

RTX A6000 48GB
llama.cppUD-Q3_K_XL10188.62113,916 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

101 tok/s

Prefill

88.6 tok/s

Total

211 tok/s

TTFT

3,916 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-q3kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next/UD-Q3_K_XL/Qwen3.8-Flash-Next-UD-Q3_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-q3kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 101.3 tok/s is the aggregate decode rate across all 12 streams, about 8.4 tok/s per stream, and the 3916 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 89 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47554 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-Q3_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 101.3, 104.8, 105.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:19 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

RTX A6000 48GB
llama.cppUD-IQ3_XXS14839.82658,711 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

148 tok/s

Prefill

39.8 tok/s

Total

265 tok/s

TTFT

8,711 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ3_XXS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-iq3xxs-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next-IQ3/UD-IQ3_XXS/Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-iq3xxs-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 148.2 tok/s is the aggregate decode rate across all 16 streams, about 9.3 tok/s per stream, and the 8711 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 81 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47608 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 161.1, 156.7, 148.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:19 AM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

RTX A6000 48GB
llama.cppUD-IQ3_XXS13171.22554,871 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

131 tok/s

Prefill

71.2 tok/s

Total

255 tok/s

TTFT

4,871 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ3_XXS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-iq3xxs-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next-IQ3/UD-IQ3_XXS/Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-fn-iq3xxs-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 12 requests sent at once (lmx --concurrency 12); 130.7 tok/s is the aggregate decode rate across all 12 streams, about 10.9 tok/s per stream, and the 4871 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 81 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47608 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 132.3, 130.7, 111.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 11:19 AM

Last edited

—

MiMo-V2.6-Flash-RL

311B MoE

RTX A6000 48GB
llama.cppQ2_K12.59.224.532,129 ms0k
Show all details for MiMo-V2.6-Flash-RL

Model

XiaomiMiMo/MiMo-V2.6-Flash-RL

Display name

MiMo-V2.6-Flash-RL

Base model

—

Revision

f1ef9bfdc3706af3f807752765e52b885e113aae

Family

—

Parameters

311B

Active params

—

MoE

yes

Output

12.5 tok/s

Prefill

9.2 tok/s

Total

24.5 tok/s

TTFT

32,129 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q2_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-mimo26f-q2k-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/MiMo-V2.6-Flash/MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mimo26f-q2k-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 12.5 tok/s is the aggregate decode rate across all 16 streams, about 0.8 tok/s per stream, and the 32129 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 126 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 46694 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF ggml-org/MiMo-V2.6-Flash-RL-GGUF @ f1ef9bfdc3706af3f807752765e52b885e113aae, file MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 25, 17.6, 12.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:09 AM

Last edited

—

GLM-5.3-Flash

321B

RTX A6000 48GB
llama.cppUD-Q2_K_XL16.011.730.924,401 ms0k
Show all details for GLM-5.3-Flash

Model

zai-org/GLM-5.3-Flash

Display name

GLM-5.3-Flash

Base model

—

Revision

a38483c8cd5df544f53d70fb281afe97369d5ab6

Family

—

Parameters

321B

Active params

—

MoE

no

Output

16.0 tok/s

Prefill

11.7 tok/s

Total

30.9 tok/s

TTFT

24,401 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

285

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q2_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-glm53f-q2kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/GLM-5.3-Flash/UD-Q2_K_XL/GLM-5.3-Flash-UD-Q2_K_XL-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-glm53f-q2kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 16 tok/s is the aggregate decode rate across all 16 streams, about 1.0 tok/s per stream, and the 24401 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 108 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47624 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/GLM-5.3-Flash-GGUF @ a38483c8cd5df544f53d70fb281afe97369d5ab6, file GLM-5.3-Flash-UD-Q2_K_XL-00001-of-00004.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 19.5, 12.7, 16 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

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Submitted

Oct 27, 2026, 10:49 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

RTX A6000 48GB
llama.cppUD-Q2_K_XL40.726.076.210,509 ms0k
Show all details for DeepSeek-V4-Flash-0731

Model

deepseek-ai/DeepSeek-V4-Flash-0731

Display name

DeepSeek-V4-Flash-0731

Base model

—

Revision

fbbb5b93fb787c21338159b0af3318bb3f4d9768

Family

Deepseek

Parameters

304B

Active params

—

MoE

yes

Output

40.7 tok/s

Prefill

26.0 tok/s

Total

76.2 tok/s

TTFT

10,509 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

273

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q2_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-dsv4f-q2kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/DeepSeek-V4-Flash/UD-Q2_K_XL/DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-q2kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 40.7 tok/s is the aggregate decode rate across all 16 streams, about 2.5 tok/s per stream, and the 10509 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 96 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47256 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-Q2_K_XL-00001-of-00003.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 40.7, 40.5, 40 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 10:29 AM

Last edited

—

Llama-4-Scout-17B-16E-Instruct

17B MoE · Llama

RTX A6000 48GB
llama.cppQ4_K_M64.832.71198,440 ms0k
Show all details for Llama-4-Scout-17B-16E-Instruct

Model

meta-llama/Llama-4-Scout-17B-16E-Instruct

Display name

Llama-4-Scout-17B-16E-Instruct

Base model

Llama-4-Scout-17B-16E

Revision

91d32823a1855f7ca12919dffa86701254271eb9

Family

Llama

Parameters

109B

Active params

17B

MoE

yes

Output

64.8 tok/s

Prefill

32.7 tok/s

Total

119 tok/s

TTFT

8,440 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

276

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q4_K_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-l4s-q4km-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Llama-4-Scout/meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M/meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-l4s-q4km-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 64.8 tok/s is the aggregate decode rate across all 16 streams, about 4.0 tok/s per stream, and the 8440 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 67 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47410 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF @ 91d32823a1855f7ca12919dffa86701254271eb9, file meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 67, 64.8, 42.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 10:19 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q4_K_XL3031045402,861 ms0k
Show all details for Qwen3-Coder-Next

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

—

Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

—

MoE

yes

Output

303 tok/s

Prefill

104 tok/s

Total

540 tok/s

TTFT

2,861 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q4_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

16

Concurrency

16

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-qcn-q4kxl-np16 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3-Coder-Next/Qwen3-Coder-Next-UD-Q4_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 65536 -np 16 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q4kxl-np16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (lmx --concurrency 16); 303 tok/s is the aggregate decode rate across all 16 streams, about 18.9 tok/s per stream, and the 2861 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 49 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47442 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 16 server slots of 4096 tokens each, context 65536. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-UD-Q4_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 301.6, 304.4, 303 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

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Oct 27, 2026, 10:09 AM

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