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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 5 of 8.

Qwen3.5-4B

4B · Qwen

1,475

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

438 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

1,475 tok/s

Prefill

699 tok/s

Total

3,073 tok/s

TTFT

438 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

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq4-bf16-mtp2 --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/Qwen3.5-4B --served-model-name Qwen3.5-4B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq4-bf16-mtp2 --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); 1474.6 tok/s is the aggregate decode rate across all 16 streams, about 92.2 tok/s per stream, and the 438 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: 1483.8, 1465.5, 1474.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:25 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

2,506

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

2,240 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

2,506 tok/s

Prefill

136 tok/s

Total

5,427 tok/s

TTFT

2,240 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

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq4-bf16-mtp2 --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/Qwen3.5-4B --served-model-name Qwen3.5-4B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq4-bf16-mtp2 --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); 2505.8 tok/s is the aggregate decode rate across all 128 streams, about 19.6 tok/s per stream, and the 2240 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: 2510.9, 2503.6, 2505.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:25 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

496

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

211 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

496 tok/s

Prefill

1,447 tok/s

Total

1,066 tok/s

TTFT

211 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 496.2 tok/s is the aggregate decode rate across all 8 streams, about 62.0 tok/s per stream, and the 211 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 (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 8: 496, 496.3, 496.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-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, 7:25 AM

Last edited

—

5.8

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ3_XXS

TTFT

7,168 ms

Context

2k · today

Show all run details

Model

zai-org/GLM-5.3-Flash

Display name

GLM-5.3-Flash

Base model

—

Revision

a38483c8cd5df544f53d70fb281afe97369d5ab6

Family

—

Parameters

321B

Active params

—

MoE

no

Output

5.8 tok/s

Prefill

39.9 tok/s

Total

11.8 tok/s

TTFT

7,168 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

286

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-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

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-glm53f-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/GLM-5.3-Flash-IQ3/UD-IQ3_XXS/GLM-5.3-Flash-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-glm53f-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. 4 requests sent at once (lmx --concurrency 4); 5.8 tok/s is the aggregate decode rate across all 4 streams, about 1.4 tok/s per stream, and the 7168 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 120 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; 47870 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/GLM-5.3-Flash-GGUF @ a38483c8cd5df544f53d70fb281afe97369d5ab6, file GLM-5.3-Flash-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: 5.8, 8.4, 8.6 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, 7:25 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

1,933

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

1,081 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

1,933 tok/s

Prefill

283 tok/s

Total

4,178 tok/s

TTFT

1,081 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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 1933.4 tok/s is the aggregate decode rate across all 64 streams, about 30.2 tok/s per stream, and the 1081 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: 1932.7, 1935.3, 1933.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:15 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

269

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

135 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

269 tok/s

Prefill

2,261 tok/s

Total

577 tok/s

TTFT

135 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 268.8 tok/s is the aggregate decode rate across all 4 streams, about 67.2 tok/s per stream, and the 135 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 (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 269.1, 268.6, 268.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:14 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

1,372

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

552 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

1,372 tok/s

Prefill

549 tok/s

Total

2,953 tok/s

TTFT

552 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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 1371.7 tok/s is the aggregate decode rate across all 32 streams, about 42.9 tok/s per stream, and the 552 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: 1372.9, 1365.3, 1371.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:14 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

2,566

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

4,026 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

2,566 tok/s

Prefill

76.2 tok/s

Total

5,587 tok/s

TTFT

4,026 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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 2565.9 tok/s is the aggregate decode rate across all 256 streams, about 10.0 tok/s per stream, and the 4026 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: 2562.8, 2566.1, 2565.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:14 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

877

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

382 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

877 tok/s

Prefill

796 tok/s

Total

1,879 tok/s

TTFT

382 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 876.5 tok/s is the aggregate decode rate across all 16 streams, about 54.8 tok/s per stream, and the 382 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 16: 876.5, 874.5, 878.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:14 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

2,364

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · BF16

TTFT

1,920 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

2,364 tok/s

Prefill

158 tok/s

Total

5,127 tok/s

TTFT

1,920 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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 2363.6 tok/s is the aggregate decode rate across all 128 streams, about 18.5 tok/s per stream, and the 1920 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: 2362.1, 2363.6, 2364.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:04 AM

Last edited

—

541

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

285 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

541 tok/s

Prefill

1,045 tok/s

Total

1,147 tok/s

TTFT

285 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

298

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

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

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-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. 8 requests sent at once (batch 8, lmx --concurrency 8); 541.1 tok/s is the aggregate decode rate across all 8 streams, about 67.6 tok/s per stream, and the 285 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 (298 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 8: 553.5, 541.1, 562.5 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, 7:04 AM

Last edited

—

1,745

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

1,214 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

1,745 tok/s

Prefill

244 tok/s

Total

3,722 tok/s

TTFT

1,214 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

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

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

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-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. 64 requests sent at once (batch 64, lmx --concurrency 64); 1745.4 tok/s is the aggregate decode rate across all 64 streams, about 27.3 tok/s per stream, and the 1214 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 64: 1785.1, 1752.8, 1745.4 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. 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, 7:04 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

1,560 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

61.2 tok/s

Prefill

194 tok/s

Total

130 tok/s

TTFT

1,560 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

4096

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

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

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-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. 4 requests sent at once (batch 4, lmx --concurrency 4); 61.2 tok/s is the aggregate decode rate across all 4 streams, about 15.3 tok/s per stream, and the 1560 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 (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 61.1, 61.2, 61.2 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 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 7:04 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

3,062 ms

Context

4k · today

Show all run details

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

116 tok/s

Prefill

99.0 tok/s

Total

237 tok/s

TTFT

3,062 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

4096

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

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

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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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. 8 requests sent at once (batch 8, lmx --concurrency 8); 115.5 tok/s is the aggregate decode rate across all 8 streams, about 14.4 tok/s per stream, and the 3062 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 (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 8: 115.7, 115.5, 115.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl31-70b-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, 7:04 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

1,562 ms

Context

4k · today

Show all run details

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

60.6 tok/s

Prefill

194 tok/s

Total

129 tok/s

TTFT

1,562 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

4096

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

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

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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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. 4 requests sent at once (batch 4, lmx --concurrency 4); 60.6 tok/s is the aggregate decode rate across all 4 streams, about 15.2 tok/s per stream, and the 1562 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 (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 60.3, 60.6, 61.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl31-70b-awq, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:54 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

9,210 ms

Context

4k · today

Show all run details

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

259 tok/s

Prefill

33.3 tok/s

Total

555 tok/s

TTFT

9,210 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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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); 259.2 tok/s is the aggregate decode rate across all 32 streams, about 8.1 tok/s per stream, and the 9210 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: 260, 260.1, 259.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl31-70b-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, 6:54 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

647 ms

Context

4k · today

Show all run details

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

32.1 tok/s

Prefill

511 tok/s

Total

68.0 tok/s

TTFT

647 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

302

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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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); 32.1 tok/s is the aggregate decode rate across all 2 streams, about 16.1 tok/s per stream, and the 647 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 (302 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 32.4, 32.1, 32.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl31-70b-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, 6:54 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

5,085 ms

Context

4k · today

Show all run details

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

187 tok/s

Prefill

61.9 tok/s

Total

396 tok/s

TTFT

5,085 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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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); 187 tok/s is the aggregate decode rate across all 16 streams, about 11.7 tok/s per stream, and the 5085 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: 189.8, 187, 187.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl31-70b-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, 6:54 AM

Last edited

—

9.5

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ3_XXS

TTFT

4,257 ms

Context

2k · today

Show all run details

Model

zai-org/GLM-5.3-Flash

Display name

GLM-5.3-Flash

Base model

—

Revision

a38483c8cd5df544f53d70fb281afe97369d5ab6

Family

—

Parameters

321B

Active params

—

MoE

no

Output

9.5 tok/s

Prefill

66.9 tok/s

Total

17.4 tok/s

TTFT

4,257 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

285

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-glm53f-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/GLM-5.3-Flash-IQ3/UD-IQ3_XXS/GLM-5.3-Flash-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-glm53f-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 120 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; 47870 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/GLM-5.3-Flash-GGUF @ a38483c8cd5df544f53d70fb281afe97369d5ab6, file GLM-5.3-Flash-UD-IQ3_XXS-00001-of-00004.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 9.3, 9.5, 9.7 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, 6:54 AM

Last edited

—

Hardware

RTX A6000 48GB

Engine

vllm · AWQ

TTFT

408 ms

Context

4k · today

Show all run details

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

16.6 tok/s

Prefill

738 tok/s

Total

35.3 tok/s

TTFT

408 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

301

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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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 vl31-70b-awq, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 16.6, 16.4, 16.6 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, 6:44 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

316

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q3_K_XL

TTFT

1,466 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

316 tok/s

Prefill

202 tok/s

Total

556 tok/s

TTFT

1,466 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-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-q3kxl-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-Q3_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-q3kxl-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); 315.6 tok/s is the aggregate decode rate across all 8 streams, about 39.5 tok/s per stream, and the 1466 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-Q3_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 315.6, 317.3, 308 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 27, 2026, 6:44 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

269

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q3_K_XL

TTFT

737 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

269 tok/s

Prefill

401 tok/s

Total

486 tok/s

TTFT

737 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-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-q3kxl-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-Q3_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-q3kxl-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); 269.4 tok/s is the aggregate decode rate across all 4 streams, about 67.3 tok/s per stream, and the 737 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-Q3_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 269.4, 268.6, 268.6 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, 6:44 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

191

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q3_K_XL

TTFT

432 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

191 tok/s

Prefill

681 tok/s

Total

353 tok/s

TTFT

432 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

294

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-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-qcn-q3kxl-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-Q3_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-q3kxl-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); 190.7 tok/s is the aggregate decode rate across all 2 streams, about 95.3 tok/s per stream, and the 432 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-Q3_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 189.9, 191.3, 190.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 27, 2026, 6:43 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

23.0

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ3_XXS

TTFT

3,321 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

23.0 tok/s

Prefill

81.6 tok/s

Total

44.1 tok/s

TTFT

3,321 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

271

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-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

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-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. 4 requests sent at once (lmx --concurrency 4); 23 tok/s is the aggregate decode rate across all 4 streams, about 5.8 tok/s per stream, and the 3321 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: 23, 27.8, 26.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 6:43 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

112

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q3_K_XL

TTFT

243 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

112 tok/s

Prefill

1,196 tok/s

Total

216 tok/s

TTFT

243 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

291

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-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-q3kxl-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-Q3_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-q3kxl-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-Q3_K_XL.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 112.1, 111.5, 110.6 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

—

ModelHardwareEnginetok/s outprefilltok/s totalTTFTDepthShare
Qwen3.5-4B

4B · Qwen

RTX A6000 48GB
vllmBF161,4756993,073438 ms0k
Show all details for Qwen3.5-4B

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

1,475 tok/s

Prefill

699 tok/s

Total

3,073 tok/s

TTFT

438 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

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq4-bf16-mtp2 --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/Qwen3.5-4B --served-model-name Qwen3.5-4B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq4-bf16-mtp2 --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); 1474.6 tok/s is the aggregate decode rate across all 16 streams, about 92.2 tok/s per stream, and the 438 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: 1483.8, 1465.5, 1474.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:25 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

RTX A6000 48GB
vllmBF162,5061365,4272,240 ms0k
Show all details for Qwen3.5-4B

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

2,506 tok/s

Prefill

136 tok/s

Total

5,427 tok/s

TTFT

2,240 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

yes

Spec method

MTP

Spec model

—

Spec draft model

—

Spec tokens

2

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq4-bf16-mtp2 --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/Qwen3.5-4B --served-model-name Qwen3.5-4B --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}' --speculative-config '{"method":"mtp","num_speculative_tokens":2}'

Extra flags

--d --name lsv-vq4-bf16-mtp2 --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); 2505.8 tok/s is the aggregate decode rate across all 128 streams, about 19.6 tok/s per stream, and the 2240 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: 2510.9, 2503.6, 2505.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16-mtp2, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 7:25 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

RTX A6000 48GB
vllmBF164961,4471,066211 ms0k
Show all details for Qwen3.5-4B

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

496 tok/s

Prefill

1,447 tok/s

Total

1,066 tok/s

TTFT

211 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 496.2 tok/s is the aggregate decode rate across all 8 streams, about 62.0 tok/s per stream, and the 211 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 (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 8: 496, 496.3, 496.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-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, 7:25 AM

Last edited

—

GLM-5.3-Flash

321B

RTX A6000 48GB
llama.cppUD-IQ3_XXS5.839.911.87,168 ms0k
Show all details for GLM-5.3-Flash

Model

zai-org/GLM-5.3-Flash

Display name

GLM-5.3-Flash

Base model

—

Revision

a38483c8cd5df544f53d70fb281afe97369d5ab6

Family

—

Parameters

321B

Active params

—

MoE

no

Output

5.8 tok/s

Prefill

39.9 tok/s

Total

11.8 tok/s

TTFT

7,168 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

286

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-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

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-glm53f-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/GLM-5.3-Flash-IQ3/UD-IQ3_XXS/GLM-5.3-Flash-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-glm53f-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. 4 requests sent at once (lmx --concurrency 4); 5.8 tok/s is the aggregate decode rate across all 4 streams, about 1.4 tok/s per stream, and the 7168 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 120 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; 47870 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/GLM-5.3-Flash-GGUF @ a38483c8cd5df544f53d70fb281afe97369d5ab6, file GLM-5.3-Flash-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: 5.8, 8.4, 8.6 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, 7:25 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

RTX A6000 48GB
vllmBF161,9332834,1781,081 ms0k
Show all details for Qwen3.5-4B

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

1,933 tok/s

Prefill

283 tok/s

Total

4,178 tok/s

TTFT

1,081 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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 1933.4 tok/s is the aggregate decode rate across all 64 streams, about 30.2 tok/s per stream, and the 1081 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: 1932.7, 1935.3, 1933.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:15 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

RTX A6000 48GB
vllmBF162692,261577135 ms0k
Show all details for Qwen3.5-4B

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

269 tok/s

Prefill

2,261 tok/s

Total

577 tok/s

TTFT

135 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 268.8 tok/s is the aggregate decode rate across all 4 streams, about 67.2 tok/s per stream, and the 135 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 (305 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 269.1, 268.6, 268.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 7:14 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

RTX A6000 48GB
vllmBF161,3725492,953552 ms0k
Show all details for Qwen3.5-4B

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

1,372 tok/s

Prefill

549 tok/s

Total

2,953 tok/s

TTFT

552 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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 1371.7 tok/s is the aggregate decode rate across all 32 streams, about 42.9 tok/s per stream, and the 552 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: 1372.9, 1365.3, 1371.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 7:14 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

RTX A6000 48GB
vllmBF162,56676.25,5874,026 ms0k
Show all details for Qwen3.5-4B

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

2,566 tok/s

Prefill

76.2 tok/s

Total

5,587 tok/s

TTFT

4,026 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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 2565.9 tok/s is the aggregate decode rate across all 256 streams, about 10.0 tok/s per stream, and the 4026 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: 2562.8, 2566.1, 2565.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 7:14 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

RTX A6000 48GB
vllmBF168777961,879382 ms0k
Show all details for Qwen3.5-4B

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

877 tok/s

Prefill

796 tok/s

Total

1,879 tok/s

TTFT

382 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 876.5 tok/s is the aggregate decode rate across all 16 streams, about 54.8 tok/s per stream, and the 382 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 16: 876.5, 874.5, 878.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 7:14 AM

Last edited

—

Qwen3.5-4B

4B · Qwen

RTX A6000 48GB
vllmBF162,3641585,1271,920 ms0k
Show all details for Qwen3.5-4B

Model

Qwen/Qwen3.5-4B

Display name

Qwen3.5-4B

Base model

Qwen3.5-4B-Base

Revision

851bf6e806efd8d0a36b00ddf55e13ccb7b8cd0a

Family

Qwen

Parameters

4B

Active params

—

MoE

no

Output

2,364 tok/s

Prefill

158 tok/s

Total

5,127 tok/s

TTFT

1,920 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-vq4-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/Qwen3.5-4B --served-model-name Qwen3.5-4B --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-vq4-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); 2363.6 tok/s is the aggregate decode rate across all 128 streams, about 18.5 tok/s per stream, and the 1920 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: 2362.1, 2363.6, 2364.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq4-bf16, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 7:04 AM

Last edited

—

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

3B MoE · Qwen

RTX A6000 48GB
vllmFP85411,0451,147285 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

541 tok/s

Prefill

1,045 tok/s

Total

1,147 tok/s

TTFT

285 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

298

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

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

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-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. 8 requests sent at once (batch 8, lmx --concurrency 8); 541.1 tok/s is the aggregate decode rate across all 8 streams, about 67.6 tok/s per stream, and the 285 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 (298 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 8: 553.5, 541.1, 562.5 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

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Submitted

Oct 27, 2026, 7:04 AM

Last edited

—

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

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,7452443,7221,214 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

1,745 tok/s

Prefill

244 tok/s

Total

3,722 tok/s

TTFT

1,214 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

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

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

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-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. 64 requests sent at once (batch 64, lmx --concurrency 64); 1745.4 tok/s is the aggregate decode rate across all 64 streams, about 27.3 tok/s per stream, and the 1214 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 64: 1785.1, 1752.8, 1745.4 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. 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

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Submitted

Oct 27, 2026, 7:04 AM

Last edited

—

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

70B · Llama

RTX A6000 48GB
vllmAWQ61.21941301,560 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

61.2 tok/s

Prefill

194 tok/s

Total

130 tok/s

TTFT

1,560 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

4096

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

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

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-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. 4 requests sent at once (batch 4, lmx --concurrency 4); 61.2 tok/s is the aggregate decode rate across all 4 streams, about 15.3 tok/s per stream, and the 1560 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 (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 61.1, 61.2, 61.2 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 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 7:04 AM

Last edited

—

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

70B · Llama

RTX A6000 48GB
vllmAWQ11699.02373,062 ms0k
Show all details for Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

116 tok/s

Prefill

99.0 tok/s

Total

237 tok/s

TTFT

3,062 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

4096

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

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

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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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. 8 requests sent at once (batch 8, lmx --concurrency 8); 115.5 tok/s is the aggregate decode rate across all 8 streams, about 14.4 tok/s per stream, and the 3062 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 (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 8: 115.7, 115.5, 115.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl31-70b-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, 7:04 AM

Last edited

—

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

70B · Llama

RTX A6000 48GB
vllmAWQ60.61941291,562 ms0k
Show all details for Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

60.6 tok/s

Prefill

194 tok/s

Total

129 tok/s

TTFT

1,562 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

4096

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

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

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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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. 4 requests sent at once (batch 4, lmx --concurrency 4); 60.6 tok/s is the aggregate decode rate across all 4 streams, about 15.2 tok/s per stream, and the 1562 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 (303 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 4: 60.3, 60.6, 61.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl31-70b-awq, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 6:54 AM

Last edited

—

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

70B · Llama

RTX A6000 48GB
vllmAWQ25933.35559,210 ms0k
Show all details for Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

259 tok/s

Prefill

33.3 tok/s

Total

555 tok/s

TTFT

9,210 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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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); 259.2 tok/s is the aggregate decode rate across all 32 streams, about 8.1 tok/s per stream, and the 9210 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: 260, 260.1, 259.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl31-70b-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, 6:54 AM

Last edited

—

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

70B · Llama

RTX A6000 48GB
vllmAWQ32.151168.0647 ms0k
Show all details for Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

32.1 tok/s

Prefill

511 tok/s

Total

68.0 tok/s

TTFT

647 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

302

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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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); 32.1 tok/s is the aggregate decode rate across all 2 streams, about 16.1 tok/s per stream, and the 647 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 (302 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 32.4, 32.1, 32.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl31-70b-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, 6:54 AM

Last edited

—

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

70B · Llama

RTX A6000 48GB
vllmAWQ18761.93965,085 ms0k
Show all details for Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

187 tok/s

Prefill

61.9 tok/s

Total

396 tok/s

TTFT

5,085 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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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); 187 tok/s is the aggregate decode rate across all 16 streams, about 11.7 tok/s per stream, and the 5085 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: 189.8, 187, 187.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vl31-70b-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, 6:54 AM

Last edited

—

GLM-5.3-Flash

321B

RTX A6000 48GB
llama.cppUD-IQ3_XXS9.566.917.44,257 ms0k
Show all details for GLM-5.3-Flash

Model

zai-org/GLM-5.3-Flash

Display name

GLM-5.3-Flash

Base model

—

Revision

a38483c8cd5df544f53d70fb281afe97369d5ab6

Family

—

Parameters

321B

Active params

—

MoE

no

Output

9.5 tok/s

Prefill

66.9 tok/s

Total

17.4 tok/s

TTFT

4,257 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

285

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-glm53f-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/GLM-5.3-Flash-IQ3/UD-IQ3_XXS/GLM-5.3-Flash-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-glm53f-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 120 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; 47870 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/GLM-5.3-Flash-GGUF @ a38483c8cd5df544f53d70fb281afe97369d5ab6, file GLM-5.3-Flash-UD-IQ3_XXS-00001-of-00004.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 9.3, 9.5, 9.7 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, 6:54 AM

Last edited

—

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

70B · Llama

RTX A6000 48GB
vllmAWQ16.673835.3408 ms0k
Show all details for Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Model

hugging-quants/Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Display name

Meta-Llama-3.1-70B-Instruct-AWQ-INT4

Base model

—

Revision

2123003760781134cfc31124aa6560a45b491fdf

Family

Llama

Parameters

70B

Active params

—

MoE

no

Output

16.6 tok/s

Prefill

738 tok/s

Total

35.3 tok/s

TTFT

408 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

301

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-vl31-70b-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-3.1-70B-AWQ --served-model-name Llama-3.1-70B-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-vl31-70b-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 vl31-70b-awq, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 16.6, 16.4, 16.6 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, 6:44 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q3_K_XL3162025561,466 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

316 tok/s

Prefill

202 tok/s

Total

556 tok/s

TTFT

1,466 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-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-q3kxl-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-Q3_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-q3kxl-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); 315.6 tok/s is the aggregate decode rate across all 8 streams, about 39.5 tok/s per stream, and the 1466 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-Q3_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 315.6, 317.3, 308 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

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Submitted

Oct 27, 2026, 6:44 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q3_K_XL269401486737 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

269 tok/s

Prefill

401 tok/s

Total

486 tok/s

TTFT

737 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-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-q3kxl-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-Q3_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-q3kxl-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); 269.4 tok/s is the aggregate decode rate across all 4 streams, about 67.3 tok/s per stream, and the 737 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-Q3_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 269.4, 268.6, 268.6 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, 6:44 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q3_K_XL191681353432 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

191 tok/s

Prefill

681 tok/s

Total

353 tok/s

TTFT

432 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

294

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-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-qcn-q3kxl-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-Q3_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-q3kxl-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); 190.7 tok/s is the aggregate decode rate across all 2 streams, about 95.3 tok/s per stream, and the 432 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-Q3_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 189.9, 191.3, 190.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 27, 2026, 6:43 AM

Last edited

—

DeepSeek-V4-Flash-0731

304B MoE · Deepseek

RTX A6000 48GB
llama.cppUD-IQ3_XXS23.081.644.13,321 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

23.0 tok/s

Prefill

81.6 tok/s

Total

44.1 tok/s

TTFT

3,321 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

271

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-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

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-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. 4 requests sent at once (lmx --concurrency 4); 23 tok/s is the aggregate decode rate across all 4 streams, about 5.8 tok/s per stream, and the 3321 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: 23, 27.8, 26.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 6:43 AM

Last edited

—

Qwen3-Coder-Next

80B MoE · Qwen

RTX A6000 48GB
llama.cppUD-Q3_K_XL1121,196216243 ms0k
Show all details for Qwen3-Coder-Next

Model

Qwen/Qwen3-Coder-Next

Display name

Qwen3-Coder-Next

Base model

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Revision

ce09c67b53bc8739eef83fe67b2f5d293c270632

Family

Qwen

Parameters

80B

Active params

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MoE

yes

Output

112 tok/s

Prefill

1,196 tok/s

Total

216 tok/s

TTFT

243 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

291

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

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GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q3_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-q3kxl-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-Q3_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-q3kxl-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-Q3_K_XL.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 112.1, 111.5, 110.6 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

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Submitted

Oct 27, 2026, 6:33 AM

Last edited

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