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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 6 of 8.

Qwen3-Coder-Next

80B MoE · Qwen

351

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

1,530 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

351 tok/s

Prefill

193 tok/s

Total

600 tok/s

TTFT

1,530 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

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

8

Concurrency

8

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-q2kxl-np8 --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-Q2_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q2kxl-np8 --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. 8 requests sent at once (lmx --concurrency 8); 351.4 tok/s is the aggregate decode rate across all 8 streams, about 43.9 tok/s per stream, and the 1530 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, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-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: 352.6, 346.4, 351.4 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, 6:33 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

274

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

800 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

274 tok/s

Prefill

370 tok/s

Total

486 tok/s

TTFT

800 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

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

8

Concurrency

4

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-q2kxl-np8 --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-Q2_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q2kxl-np8 --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. 4 requests sent at once (lmx --concurrency 4); 273.7 tok/s is the aggregate decode rate across all 4 streams, about 68.4 tok/s per stream, and the 800 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, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-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: 273.7, 273.2, 270.1 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, 6:33 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

16.0

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ3_XXS

TTFT

2,949 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

16.0 tok/s

Prefill

93.6 tok/s

Total

30.4 tok/s

TTFT

2,949 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

276

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

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

8

Concurrency

2

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-iq3xxs-np8 --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-IQ3/UD-IQ3_XXS/DeepSeek-V4-Flash-0731-UD-IQ3_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq3xxs-np8 --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. 2 requests sent at once (lmx --concurrency 2); 16 tok/s is the aggregate decode rate across all 2 streams, about 8.0 tok/s per stream, and the 2949 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 104 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; 47716 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ3_XXS-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: 22.3, 20.9, 16 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, 6:33 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

16.6

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ3_XXS

TTFT

2,334 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

16.6 tok/s

Prefill

117 tok/s

Total

30.0 tok/s

TTFT

2,334 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

274

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

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

8

Concurrency

1

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-iq3xxs-np8 --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-IQ3/UD-IQ3_XXS/DeepSeek-V4-Flash-0731-UD-IQ3_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq3xxs-np8 --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

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 104 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; 47716 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ3_XXS-00001-of-00004.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 16.3, 16.6, 16.7 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

Reactions

—

Submitted

Oct 27, 2026, 6:33 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

7,796 ms

Context

4k · today

Show all run details

Model

ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Base model

Llama-3.3-70B-Instruct

Revision

a3c28ea93c041acfaadb85401ad6493749a5aa04

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

261 tok/s

Prefill

40.3 tok/s

Total

557 tok/s

TTFT

7,796 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

4096

Batch size

32

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

vllm

Engine version

0.32.0

Quantization

AWQ

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.9

Max running seqs

32

Scheduler delay

—

Num parallel

—

Concurrency

32

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vl33-70b-awq --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Llama-3.3-70B-AWQ --served-model-name Llama-3.3-70B-AWQ --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 4096 --gpu-memory-utilization 0.90 --max-num-seqs 32 --enforce-eager

Extra flags

--d --name lsv-vl33-70b-awq --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 4096 --enforce-eager

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 260.6 tok/s is the aggregate decode rate across all 32 streams, about 8.1 tok/s per stream, and the 7796 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs 32, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 32: 258.8, 260.6, 261.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl33-70b-awq, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 6:23 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

652 ms

Context

4k · today

Show all run details

Model

ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Base model

Llama-3.3-70B-Instruct

Revision

a3c28ea93c041acfaadb85401ad6493749a5aa04

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

31.5 tok/s

Prefill

512 tok/s

Total

67.1 tok/s

TTFT

652 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

4096

Batch size

2

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

vllm

Engine version

0.32.0

Quantization

AWQ

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.9

Max running seqs

32

Scheduler delay

—

Num parallel

—

Concurrency

2

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vl33-70b-awq --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Llama-3.3-70B-AWQ --served-model-name Llama-3.3-70B-AWQ --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 4096 --gpu-memory-utilization 0.90 --max-num-seqs 32 --enforce-eager

Extra flags

--d --name lsv-vl33-70b-awq --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 4096 --enforce-eager

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 31.5 tok/s is the aggregate decode rate across all 2 streams, about 15.8 tok/s per stream, and the 652 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs 32, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 31.5, 31.2, 31.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl33-70b-awq, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:23 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

5,095 ms

Context

4k · today

Show all run details

Model

ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Base model

Llama-3.3-70B-Instruct

Revision

a3c28ea93c041acfaadb85401ad6493749a5aa04

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

187 tok/s

Prefill

62.1 tok/s

Total

396 tok/s

TTFT

5,095 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

4096

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

vllm

Engine version

0.32.0

Quantization

AWQ

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.9

Max running seqs

32

Scheduler delay

—

Num parallel

—

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vl33-70b-awq --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Llama-3.3-70B-AWQ --served-model-name Llama-3.3-70B-AWQ --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 4096 --gpu-memory-utilization 0.90 --max-num-seqs 32 --enforce-eager

Extra flags

--d --name lsv-vl33-70b-awq --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 4096 --enforce-eager

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (batch 16, lmx --concurrency 16); 186.9 tok/s is the aggregate decode rate across all 16 streams, about 11.7 tok/s per stream, and the 5095 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs 32, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 16: 186.9, 186.9, 186.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl33-70b-awq, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:23 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

410 ms

Context

4k · today

Show all run details

Model

ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Base model

Llama-3.3-70B-Instruct

Revision

a3c28ea93c041acfaadb85401ad6493749a5aa04

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

16.4 tok/s

Prefill

739 tok/s

Total

35.0 tok/s

TTFT

410 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

4096

Batch size

1

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

vllm

Engine version

0.32.0

Quantization

AWQ

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.9

Max running seqs

32

Scheduler delay

—

Num parallel

—

Concurrency

1

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vl33-70b-awq --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Llama-3.3-70B-AWQ --served-model-name Llama-3.3-70B-AWQ --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 4096 --gpu-memory-utilization 0.90 --max-num-seqs 32 --enforce-eager

Extra flags

--d --name lsv-vl33-70b-awq --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 4096 --enforce-eager

Notes

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. Config vl33-70b-awq, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 16.4, 16.5, 16.4 tok/s; this is the median (declared rule, tie to the earlier). Prompt/output samples, engineTimingsRaw and MTP counts were added from the lmx run file and exact per-request vLLM /metrics deltas, because lmx does not send them.

Reactions

—

Submitted

Oct 27, 2026, 6:23 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

115

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

270 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

115 tok/s

Prefill

1,088 tok/s

Total

221 tok/s

TTFT

270 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

294

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

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

8

Concurrency

1

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-q2kxl-np8 --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-Q2_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q2kxl-np8 --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

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, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-UD-Q2_K_XL.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 115.3, 114.8, 114.1 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

Reactions

—

Submitted

Oct 27, 2026, 6:23 AM

Last edited

—

344

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

177 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

344 tok/s

Prefill

1,689 tok/s

Total

727 tok/s

TTFT

177 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

4

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (batch 4, lmx --concurrency 4); 344.1 tok/s is the aggregate decode rate across all 4 streams, about 86.0 tok/s per stream, and the 177 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 364.2, 364.3, 344.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 6:13 AM

Last edited

—

1,288

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

740 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,288 tok/s

Prefill

400 tok/s

Total

2,740 tok/s

TTFT

740 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

32

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 1288.1 tok/s is the aggregate decode rate across all 32 streams, about 40.3 tok/s per stream, and the 740 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 32: 1276.7, 1288.1, 1338.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 6:13 AM

Last edited

—

2,559

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

4,880 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

2,559 tok/s

Prefill

61.0 tok/s

Total

5,492 tok/s

TTFT

4,880 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 2558.8 tok/s is the aggregate decode rate across all 256 streams, about 10.0 tok/s per stream, and the 4880 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 2558.8, 2554.6, 2561.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:13 AM

Last edited

—

871

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

423 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

871 tok/s

Prefill

748 tok/s

Total

1,833 tok/s

TTFT

423 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (batch 16, lmx --concurrency 16); 871.4 tok/s is the aggregate decode rate across all 16 streams, about 54.5 tok/s per stream, and the 423 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 16: 871.4, 902.5, 848.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 6:13 AM

Last edited

—

2,254

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

2,425 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

2,254 tok/s

Prefill

123 tok/s

Total

4,827 tok/s

TTFT

2,425 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

128

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

128

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 2254.4 tok/s is the aggregate decode rate across all 128 streams, about 17.6 tok/s per stream, and the 2425 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 2240.9, 2254.4, 2254.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:13 AM

Last edited

—

486

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

25,919 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen2.5-32B-Instruct-AWQ

Display name

Qwen2.5-32B-Instruct-AWQ

Base model

Qwen2.5-32B-Instruct

Revision

5c7cb76a268fc6cfbb9c4777eb24ba6e27f9ee6c

Family

Qwen

Parameters

32B

Active params

—

MoE

no

Output

486 tok/s

Prefill

12.3 tok/s

Total

1,086 tok/s

TTFT

25,919 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

317

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

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

vllm

Engine version

0.32.0

Quantization

AWQ

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq25-32b-awq --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen2.5-32B-AWQ --served-model-name Qwen2.5-32B-AWQ --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq25-32b-awq --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 486.1 tok/s is the aggregate decode rate across all 256 streams, about 1.9 tok/s per stream, and the 25919 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (317 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 486.6, 486.1, 486 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq25-32b-awq, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:03 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

882

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

2,456 ms

Context

33k · today

Show all run details

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

882 tok/s

Prefill

123 tok/s

Total

1,914 tok/s

TTFT

2,456 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

64

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

64

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 64 requests sent at once (batch 64, lmx --concurrency 64); 881.5 tok/s is the aggregate decode rate across all 64 streams, about 13.8 tok/s per stream, and the 2456 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 64: 881.5, 880.7, 881.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:03 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

583

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

1,281 ms

Context

33k · today

Show all run details

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

583 tok/s

Prefill

237 tok/s

Total

1,265 tok/s

TTFT

1,281 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

32

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 583.1 tok/s is the aggregate decode rate across all 32 streams, about 18.2 tok/s per stream, and the 1281 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 32: 583.5, 583.1, 583 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:02 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

990

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

9,546 ms

Context

33k · today

Show all run details

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

990 tok/s

Prefill

31.8 tok/s

Total

2,161 tok/s

TTFT

9,546 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 989.7 tok/s is the aggregate decode rate across all 256 streams, about 3.9 tok/s per stream, and the 9546 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 989.7, 989.5, 990.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:02 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

985

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

4,792 ms

Context

33k · today

Show all run details

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

985 tok/s

Prefill

63.3 tok/s

Total

2,144 tok/s

TTFT

4,792 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

128

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

128

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 984.5 tok/s is the aggregate decode rate across all 128 streams, about 7.7 tok/s per stream, and the 4792 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 983.6, 984.6, 984.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:02 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

191

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

618 ms

Context

33k · today

Show all run details

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

191 tok/s

Prefill

492 tok/s

Total

413 tok/s

TTFT

618 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

8

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

8

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (batch 8, lmx --concurrency 8); 191 tok/s is the aggregate decode rate across all 8 streams, about 23.9 tok/s per stream, and the 618 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 8: 191, 191, 190.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 5:52 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

101

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

359 ms

Context

33k · today

Show all run details

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

101 tok/s

Prefill

847 tok/s

Total

219 tok/s

TTFT

359 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

4

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (batch 4, lmx --concurrency 4); 101.3 tok/s is the aggregate decode rate across all 4 streams, about 25.3 tok/s per stream, and the 359 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 101.4, 101.3, 101.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 5:52 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

52.3

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

169 ms

Context

33k · today

Show all run details

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

52.3 tok/s

Prefill

1,989 tok/s

Total

113 tok/s

TTFT

169 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

2

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

2

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 52.3 tok/s is the aggregate decode rate across all 2 streams, about 26.1 tok/s per stream, and the 169 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 52.3, 52.3, 52.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 5:52 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

340

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

694 ms

Context

33k · today

Show all run details

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

340 tok/s

Prefill

435 tok/s

Total

735 tok/s

TTFT

694 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (batch 16, lmx --concurrency 16); 339.6 tok/s is the aggregate decode rate across all 16 streams, about 21.2 tok/s per stream, and the 694 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 16: 340, 339.8, 339.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 5:52 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

27.0

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

110 ms

Context

33k · today

Show all run details

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

27.0 tok/s

Prefill

2,758 tok/s

Total

58.6 tok/s

TTFT

110 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

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. Config vg4-12b-bf16, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 27.1, 27, 27 tok/s; this is the median (declared rule, tie to the earlier). Prompt/output samples, engineTimingsRaw and MTP counts were added from the lmx run file and exact per-request vLLM /metrics deltas, because lmx does not send them.

Reactions

—

Submitted

Oct 27, 2026, 5:52 AM

Last edited

—

126

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

63.5 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

126 tok/s

Prefill

4,675 tok/s

Total

265 tok/s

TTFT

63.5 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

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. Config vq3c-30b-fp8, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 126.5, 126, 125.6 tok/s; this is the median (declared rule, tie to the earlier). Prompt/output samples, engineTimingsRaw and MTP counts were added from the lmx run file and exact per-request vLLM /metrics deltas, because lmx does not send them.

Reactions

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Submitted

Oct 27, 2026, 5:42 AM

Last edited

—

ModelHardwareEnginetok/s outprefilltok/s totalTTFTDepthShare
Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q2_K_XL3511936001,530 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

351 tok/s

Prefill

193 tok/s

Total

600 tok/s

TTFT

1,530 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

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

8

Concurrency

8

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-q2kxl-np8 --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-Q2_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q2kxl-np8 --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. 8 requests sent at once (lmx --concurrency 8); 351.4 tok/s is the aggregate decode rate across all 8 streams, about 43.9 tok/s per stream, and the 1530 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, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-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: 352.6, 346.4, 351.4 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, 6:33 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q2_K_XL274370486800 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

274 tok/s

Prefill

370 tok/s

Total

486 tok/s

TTFT

800 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

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

8

Concurrency

4

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-q2kxl-np8 --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-Q2_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q2kxl-np8 --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. 4 requests sent at once (lmx --concurrency 4); 273.7 tok/s is the aggregate decode rate across all 4 streams, about 68.4 tok/s per stream, and the 800 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, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-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: 273.7, 273.2, 270.1 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, 6:33 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

RTX A6000 48GB
llama.cppUD-IQ3_XXS16.093.630.42,949 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

16.0 tok/s

Prefill

93.6 tok/s

Total

30.4 tok/s

TTFT

2,949 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

276

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

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

8

Concurrency

2

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-iq3xxs-np8 --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-IQ3/UD-IQ3_XXS/DeepSeek-V4-Flash-0731-UD-IQ3_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq3xxs-np8 --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. 2 requests sent at once (lmx --concurrency 2); 16 tok/s is the aggregate decode rate across all 2 streams, about 8.0 tok/s per stream, and the 2949 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 104 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; 47716 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ3_XXS-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: 22.3, 20.9, 16 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, 6:33 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

RTX A6000 48GB
llama.cppUD-IQ3_XXS16.611730.02,334 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

16.6 tok/s

Prefill

117 tok/s

Total

30.0 tok/s

TTFT

2,334 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

274

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

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

8

Concurrency

1

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-iq3xxs-np8 --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-IQ3/UD-IQ3_XXS/DeepSeek-V4-Flash-0731-UD-IQ3_XXS-00001-of-00004.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-dsv4f-iq3xxs-np8 --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

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 104 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; 47716 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/DeepSeek-V4-Flash-0731-GGUF @ fbbb5b93fb787c21338159b0af3318bb3f4d9768, file DeepSeek-V4-Flash-0731-UD-IQ3_XXS-00001-of-00004.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 16.3, 16.6, 16.7 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

Reactions

—

Submitted

Oct 27, 2026, 6:33 AM

Last edited

—

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

70B · Llama

RTX A6000 48GB
vllmAWQ26140.35577,796 ms0k
Show all details for Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Model

ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Base model

Llama-3.3-70B-Instruct

Revision

a3c28ea93c041acfaadb85401ad6493749a5aa04

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

261 tok/s

Prefill

40.3 tok/s

Total

557 tok/s

TTFT

7,796 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

4096

Batch size

32

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

vllm

Engine version

0.32.0

Quantization

AWQ

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.9

Max running seqs

32

Scheduler delay

—

Num parallel

—

Concurrency

32

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vl33-70b-awq --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Llama-3.3-70B-AWQ --served-model-name Llama-3.3-70B-AWQ --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 4096 --gpu-memory-utilization 0.90 --max-num-seqs 32 --enforce-eager

Extra flags

--d --name lsv-vl33-70b-awq --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 4096 --enforce-eager

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 260.6 tok/s is the aggregate decode rate across all 32 streams, about 8.1 tok/s per stream, and the 7796 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs 32, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 32: 258.8, 260.6, 261.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl33-70b-awq, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 6:23 AM

Last edited

—

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

70B · Llama

RTX A6000 48GB
vllmAWQ31.551267.1652 ms0k
Show all details for Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Model

ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Base model

Llama-3.3-70B-Instruct

Revision

a3c28ea93c041acfaadb85401ad6493749a5aa04

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

31.5 tok/s

Prefill

512 tok/s

Total

67.1 tok/s

TTFT

652 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

4096

Batch size

2

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

vllm

Engine version

0.32.0

Quantization

AWQ

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.9

Max running seqs

32

Scheduler delay

—

Num parallel

—

Concurrency

2

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vl33-70b-awq --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Llama-3.3-70B-AWQ --served-model-name Llama-3.3-70B-AWQ --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 4096 --gpu-memory-utilization 0.90 --max-num-seqs 32 --enforce-eager

Extra flags

--d --name lsv-vl33-70b-awq --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 4096 --enforce-eager

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 31.5 tok/s is the aggregate decode rate across all 2 streams, about 15.8 tok/s per stream, and the 652 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs 32, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 31.5, 31.2, 31.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl33-70b-awq, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:23 AM

Last edited

—

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

70B · Llama

RTX A6000 48GB
vllmAWQ18762.13965,095 ms0k
Show all details for Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Model

ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Base model

Llama-3.3-70B-Instruct

Revision

a3c28ea93c041acfaadb85401ad6493749a5aa04

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

187 tok/s

Prefill

62.1 tok/s

Total

396 tok/s

TTFT

5,095 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

4096

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

vllm

Engine version

0.32.0

Quantization

AWQ

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.9

Max running seqs

32

Scheduler delay

—

Num parallel

—

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vl33-70b-awq --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Llama-3.3-70B-AWQ --served-model-name Llama-3.3-70B-AWQ --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 4096 --gpu-memory-utilization 0.90 --max-num-seqs 32 --enforce-eager

Extra flags

--d --name lsv-vl33-70b-awq --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 4096 --enforce-eager

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (batch 16, lmx --concurrency 16); 186.9 tok/s is the aggregate decode rate across all 16 streams, about 11.7 tok/s per stream, and the 5095 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs 32, max_model_len 4096; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 16: 186.9, 186.9, 186.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl33-70b-awq, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:23 AM

Last edited

—

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

70B · Llama

RTX A6000 48GB
vllmAWQ16.473935.0410 ms0k
Show all details for Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Model

ibnzterrell/Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.3-70B-Instruct-AWQ-INT4

Base model

Llama-3.3-70B-Instruct

Revision

a3c28ea93c041acfaadb85401ad6493749a5aa04

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

16.4 tok/s

Prefill

739 tok/s

Total

35.0 tok/s

TTFT

410 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

4096

Batch size

1

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

vllm

Engine version

0.32.0

Quantization

AWQ

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.9

Max running seqs

32

Scheduler delay

—

Num parallel

—

Concurrency

1

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vl33-70b-awq --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Llama-3.3-70B-AWQ --served-model-name Llama-3.3-70B-AWQ --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 4096 --gpu-memory-utilization 0.90 --max-num-seqs 32 --enforce-eager

Extra flags

--d --name lsv-vl33-70b-awq --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 4096 --enforce-eager

Notes

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. Config vl33-70b-awq, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 16.4, 16.5, 16.4 tok/s; this is the median (declared rule, tie to the earlier). Prompt/output samples, engineTimingsRaw and MTP counts were added from the lmx run file and exact per-request vLLM /metrics deltas, because lmx does not send them.

Reactions

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Submitted

Oct 27, 2026, 6:23 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q2_K_XL1151,088221270 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

115 tok/s

Prefill

1,088 tok/s

Total

221 tok/s

TTFT

270 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

294

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

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

8

Concurrency

1

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-q2kxl-np8 --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-Q2_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-qcn-q2kxl-np8 --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

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, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3-Coder-Next-GGUF @ ce09c67b53bc8739eef83fe67b2f5d293c270632, file Qwen3-Coder-Next-UD-Q2_K_XL.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 115.3, 114.8, 114.1 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

Reactions

—

Submitted

Oct 27, 2026, 6:23 AM

Last edited

—

Qwen3-Coder-30B-A3B-Instruct-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP83441,689727177 ms0k
Show all details for Qwen3-Coder-30B-A3B-Instruct-FP8

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

344 tok/s

Prefill

1,689 tok/s

Total

727 tok/s

TTFT

177 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

4

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (batch 4, lmx --concurrency 4); 344.1 tok/s is the aggregate decode rate across all 4 streams, about 86.0 tok/s per stream, and the 177 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 364.2, 364.3, 344.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 6:13 AM

Last edited

—

Qwen3-Coder-30B-A3B-Instruct-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,2884002,740740 ms0k
Show all details for Qwen3-Coder-30B-A3B-Instruct-FP8

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,288 tok/s

Prefill

400 tok/s

Total

2,740 tok/s

TTFT

740 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

32

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 1288.1 tok/s is the aggregate decode rate across all 32 streams, about 40.3 tok/s per stream, and the 740 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 32: 1276.7, 1288.1, 1338.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 6:13 AM

Last edited

—

Qwen3-Coder-30B-A3B-Instruct-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP82,55961.05,4924,880 ms0k
Show all details for Qwen3-Coder-30B-A3B-Instruct-FP8

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

2,559 tok/s

Prefill

61.0 tok/s

Total

5,492 tok/s

TTFT

4,880 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 2558.8 tok/s is the aggregate decode rate across all 256 streams, about 10.0 tok/s per stream, and the 4880 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 2558.8, 2554.6, 2561.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:13 AM

Last edited

—

Qwen3-Coder-30B-A3B-Instruct-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP88717481,833423 ms0k
Show all details for Qwen3-Coder-30B-A3B-Instruct-FP8

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

871 tok/s

Prefill

748 tok/s

Total

1,833 tok/s

TTFT

423 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (batch 16, lmx --concurrency 16); 871.4 tok/s is the aggregate decode rate across all 16 streams, about 54.5 tok/s per stream, and the 423 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (297 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 16: 871.4, 902.5, 848.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 6:13 AM

Last edited

—

Qwen3-Coder-30B-A3B-Instruct-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP82,2541234,8272,425 ms0k
Show all details for Qwen3-Coder-30B-A3B-Instruct-FP8

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

2,254 tok/s

Prefill

123 tok/s

Total

4,827 tok/s

TTFT

2,425 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

128

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

128

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 2254.4 tok/s is the aggregate decode rate across all 128 streams, about 17.6 tok/s per stream, and the 2425 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 2240.9, 2254.4, 2254.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3c-30b-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 6:13 AM

Last edited

—

Qwen2.5-32B-Instruct-AWQ

32B · Qwen

RTX A6000 48GB
vllmAWQ48612.31,08625,919 ms0k
Show all details for Qwen2.5-32B-Instruct-AWQ

Model

Qwen/Qwen2.5-32B-Instruct-AWQ

Display name

Qwen2.5-32B-Instruct-AWQ

Base model

Qwen2.5-32B-Instruct

Revision

5c7cb76a268fc6cfbb9c4777eb24ba6e27f9ee6c

Family

Qwen

Parameters

32B

Active params

—

MoE

no

Output

486 tok/s

Prefill

12.3 tok/s

Total

1,086 tok/s

TTFT

25,919 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

317

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

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

vllm

Engine version

0.32.0

Quantization

AWQ

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq25-32b-awq --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen2.5-32B-AWQ --served-model-name Qwen2.5-32B-AWQ --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq25-32b-awq --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 486.1 tok/s is the aggregate decode rate across all 256 streams, about 1.9 tok/s per stream, and the 25919 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (317 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 486.6, 486.1, 486 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq25-32b-awq, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 6:03 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

RTX A6000 48GB
vllmBF168821231,9142,456 ms0k
Show all details for gemma-4-12B-it

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

882 tok/s

Prefill

123 tok/s

Total

1,914 tok/s

TTFT

2,456 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

64

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

64

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 64 requests sent at once (batch 64, lmx --concurrency 64); 881.5 tok/s is the aggregate decode rate across all 64 streams, about 13.8 tok/s per stream, and the 2456 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 64: 881.5, 880.7, 881.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 6:03 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

RTX A6000 48GB
vllmBF165832371,2651,281 ms0k
Show all details for gemma-4-12B-it

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

583 tok/s

Prefill

237 tok/s

Total

1,265 tok/s

TTFT

1,281 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

32

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 583.1 tok/s is the aggregate decode rate across all 32 streams, about 18.2 tok/s per stream, and the 1281 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 32: 583.5, 583.1, 583 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:02 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

RTX A6000 48GB
vllmBF1699031.82,1619,546 ms0k
Show all details for gemma-4-12B-it

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

990 tok/s

Prefill

31.8 tok/s

Total

2,161 tok/s

TTFT

9,546 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 989.7 tok/s is the aggregate decode rate across all 256 streams, about 3.9 tok/s per stream, and the 9546 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 989.7, 989.5, 990.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 6:02 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

RTX A6000 48GB
vllmBF1698563.32,1444,792 ms0k
Show all details for gemma-4-12B-it

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

985 tok/s

Prefill

63.3 tok/s

Total

2,144 tok/s

TTFT

4,792 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

128

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

128

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 984.5 tok/s is the aggregate decode rate across all 128 streams, about 7.7 tok/s per stream, and the 4792 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 983.6, 984.6, 984.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:02 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

RTX A6000 48GB
vllmBF16191492413618 ms0k
Show all details for gemma-4-12B-it

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

191 tok/s

Prefill

492 tok/s

Total

413 tok/s

TTFT

618 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

8

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

8

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (batch 8, lmx --concurrency 8); 191 tok/s is the aggregate decode rate across all 8 streams, about 23.9 tok/s per stream, and the 618 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 8: 191, 191, 190.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

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Submitted

Oct 27, 2026, 5:52 AM

Last edited

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gemma-4-12B-it

12B · Gemma

RTX A6000 48GB
vllmBF16101847219359 ms0k
Show all details for gemma-4-12B-it

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

101 tok/s

Prefill

847 tok/s

Total

219 tok/s

TTFT

359 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

4

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (batch 4, lmx --concurrency 4); 101.3 tok/s is the aggregate decode rate across all 4 streams, about 25.3 tok/s per stream, and the 359 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 101.4, 101.3, 101.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

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Submitted

Oct 27, 2026, 5:52 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

RTX A6000 48GB
vllmBF1652.31,989113169 ms0k
Show all details for gemma-4-12B-it

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

52.3 tok/s

Prefill

1,989 tok/s

Total

113 tok/s

TTFT

169 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

2

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

2

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 52.3 tok/s is the aggregate decode rate across all 2 streams, about 26.1 tok/s per stream, and the 169 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 52.3, 52.3, 52.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 5:52 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

RTX A6000 48GB
vllmBF16340435735694 ms0k
Show all details for gemma-4-12B-it

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

340 tok/s

Prefill

435 tok/s

Total

735 tok/s

TTFT

694 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 16 requests sent at once (batch 16, lmx --concurrency 16); 339.6 tok/s is the aggregate decode rate across all 16 streams, about 21.2 tok/s per stream, and the 694 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 16: 340, 339.8, 339.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vg4-12b-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 5:52 AM

Last edited

—

gemma-4-12B-it

12B · Gemma

RTX A6000 48GB
vllmBF1627.02,75858.6110 ms0k
Show all details for gemma-4-12B-it

Model

google/gemma-4-12B-it

Display name

gemma-4-12B-it

Base model

gemma-4-12B

Revision

707f0a3b8a3c7ad586ed01e27eafbad8a27dd0f7

Family

Gemma

Parameters

12B

Active params

—

MoE

no

Output

27.0 tok/s

Prefill

2,758 tok/s

Total

58.6 tok/s

TTFT

110 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

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

vllm

Engine version

0.32.0

Quantization

BF16

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vg4-12b-bf16 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/gemma-4-12B-it --served-model-name gemma-4-12B-it --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vg4-12b-bf16 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

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. Config vg4-12b-bf16, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 27.1, 27, 27 tok/s; this is the median (declared rule, tie to the earlier). Prompt/output samples, engineTimingsRaw and MTP counts were added from the lmx run file and exact per-request vLLM /metrics deltas, because lmx does not send them.

Reactions

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Submitted

Oct 27, 2026, 5:52 AM

Last edited

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Qwen3-Coder-30B-A3B-Instruct-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81264,67526563.5 ms0k
Show all details for Qwen3-Coder-30B-A3B-Instruct-FP8

Model

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

Display name

Qwen3-Coder-30B-A3B-Instruct-FP8

Base model

—

Revision

dcaee4d4dfc5ee71ad501f01f530e5652438fde0

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

126 tok/s

Prefill

4,675 tok/s

Total

265 tok/s

TTFT

63.5 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

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

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

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

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3c-30b-fp8 --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 vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34 --model /mnt/models/weights/Qwen3-Coder-30B-A3B-FP8 --served-model-name Qwen3-Coder-30B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3c-30b-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

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. Config vq3c-30b-fp8, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 126.5, 126, 125.6 tok/s; this is the median (declared rule, tie to the earlier). Prompt/output samples, engineTimingsRaw and MTP counts were added from the lmx run file and exact per-request vLLM /metrics deltas, because lmx does not send them.

Reactions

—

Submitted

Oct 27, 2026, 5:42 AM

Last edited

—