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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 8 of 8.

Qwen3.6-27B-FP8

27B · Qwen

23.6

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

252 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

23.6 tok/s

Prefill

1,203 tok/s

Total

50.7 tok/s

TTFT

252 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

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

64

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-vq36-27b-fp8-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq36-27b-fp8-ns64 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Config vq36-27b-fp8-ns64, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 23.6, 23.6, 23.6 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, 4:50 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

312

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

1,612 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

312 tok/s

Prefill

190 tok/s

Total

591 tok/s

TTFT

1,612 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

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

64

Scheduler delay

—

Num parallel

—

Concurrency

8

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --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); 312 tok/s is the aggregate decode rate across all 8 streams, about 39.0 tok/s per stream, and the 1612 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 64, 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: 314.3, 312, 309.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, 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, 4:50 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

357

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

19,044 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

357 tok/s

Prefill

16.0 tok/s

Total

764 tok/s

TTFT

19,044 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

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

64

Scheduler delay

—

Num parallel

—

Concurrency

64

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --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); 357.1 tok/s is the aggregate decode rate across all 64 streams, about 5.6 tok/s per stream, and the 19044 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 64, 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: 357.6, 357.1, 356.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:50 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

183

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

897 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

183 tok/s

Prefill

340 tok/s

Total

358 tok/s

TTFT

897 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

Scheduler delay

—

Num parallel

—

Concurrency

4

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --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); 183.2 tok/s is the aggregate decode rate across all 4 streams, about 45.8 tok/s per stream, and the 897 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 64, 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 4: 183.2, 184.9, 183 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:50 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

354

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

4,429 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

354 tok/s

Prefill

69.1 tok/s

Total

743 tok/s

TTFT

4,429 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

Scheduler delay

—

Num parallel

—

Concurrency

32

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --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); 354.4 tok/s is the aggregate decode rate across all 32 streams, about 11.1 tok/s per stream, and the 4429 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 64, 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 32: 354.2, 354.4, 354.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:40 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

98.7

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

437 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

98.7 tok/s

Prefill

716 tok/s

Total

202 tok/s

TTFT

437 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

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

64

Scheduler delay

—

Num parallel

—

Concurrency

2

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 98.7 tok/s is the aggregate decode rate across all 2 streams, about 49.4 tok/s per stream, and the 437 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 64, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 98.8, 99.8, 98.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, 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, 4:40 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

444

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

3,111 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

444 tok/s

Prefill

98.2 tok/s

Total

872 tok/s

TTFT

3,111 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

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

64

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --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); 443.9 tok/s is the aggregate decode rate across all 16 streams, about 27.7 tok/s per stream, and the 3111 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 64, 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: 443.9, 446.5, 446.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, 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, 4:40 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

51.1

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

252 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

51.1 tok/s

Prefill

1,208 tok/s

Total

107 tok/s

TTFT

252 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

Scheduler delay

—

Num parallel

—

Concurrency

1

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Config vq36-27b-fp8-mtp2-ns64, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 51.1, 50.5, 52.1 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, 4:40 AM

Last edited

—

17.2

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q2_K

TTFT

8,851 ms

Context

2k · today

Show all run details

Model

XiaomiMiMo/MiMo-V2.6-Flash-RL

Display name

MiMo-V2.6-Flash-RL

Base model

—

Revision

f1ef9bfdc3706af3f807752765e52b885e113aae

Family

—

Parameters

311B

Active params

—

MoE

yes

Output

17.2 tok/s

Prefill

33.3 tok/s

Total

34.5 tok/s

TTFT

8,851 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

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

Q2_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-mimo26f-q2k-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/MiMo-V2.6-Flash/MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mimo26f-q2k-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); 17.2 tok/s is the aggregate decode rate across all 8 streams, about 2.1 tok/s per stream, and the 8851 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 126 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47318 MiB of VRAM in use after load; a model download was running on the VM during this run, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/MiMo-V2.6-Flash-RL-GGUF @ f1ef9bfdc3706af3f807752765e52b885e113aae, file MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 13.5, 17.2, 19.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 27, 2026, 4:40 AM

Last edited

—

Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

206

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

200 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

206 tok/s

Prefill

1,529 tok/s

Total

419 tok/s

TTFT

200 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

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

64

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-vq36-35b-fp8-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq36-35b-fp8-ns64 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 206.3 tok/s is the aggregate decode rate across all 2 streams, about 103.2 tok/s per stream, and the 200 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 64, 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 2: 206.3, 207.3, 207.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-ns64, 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, 4:30 AM

Last edited

—

Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

890

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

668 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

890 tok/s

Prefill

457 tok/s

Total

1,822 tok/s

TTFT

668 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

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-vq36-35b-fp8-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq36-35b-fp8-ns64 --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); 890.4 tok/s is the aggregate decode rate across all 16 streams, about 55.6 tok/s per stream, and the 668 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 64, 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 16: 872.7, 895.5, 890.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-ns64, 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, 4:30 AM

Last edited

—

Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

126

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

111 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

126 tok/s

Prefill

2,742 tok/s

Total

263 tok/s

TTFT

111 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

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-vq36-35b-fp8-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq36-35b-fp8-ns64 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Config vq36-35b-fp8-ns64, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 126.6, 126.2, 125.8 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, 4:30 AM

Last edited

—

Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

855

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

318 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

855 tok/s

Prefill

956 tok/s

Total

1,726 tok/s

TTFT

318 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

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

64

Scheduler delay

—

Num parallel

—

Concurrency

8

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-vq36-35b-fp8-mtp2-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-35b-fp8-mtp2-ns64 --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); 855.4 tok/s is the aggregate decode rate across all 8 streams, about 106.9 tok/s per stream, and the 318 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 64, 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 8: 855.4, 818.6, 800.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-mtp2-ns64, 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, 4:30 AM

Last edited

—

25.2

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q2_K

TTFT

5,212 ms

Context

2k · today

Show all run details

Model

XiaomiMiMo/MiMo-V2.6-Flash-RL

Display name

MiMo-V2.6-Flash-RL

Base model

—

Revision

f1ef9bfdc3706af3f807752765e52b885e113aae

Family

—

Parameters

311B

Active params

—

MoE

yes

Output

25.2 tok/s

Prefill

56.6 tok/s

Total

48.0 tok/s

TTFT

5,212 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

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

Q2_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-mimo26f-q2k-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/MiMo-V2.6-Flash/MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mimo26f-q2k-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); 25.2 tok/s is the aggregate decode rate across all 4 streams, about 6.3 tok/s per stream, and the 5212 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 126 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47318 MiB of VRAM in use after load; a model download was running on the VM during this run, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/MiMo-V2.6-Flash-RL-GGUF @ f1ef9bfdc3706af3f807752765e52b885e113aae, file MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 15.7, 15.9, 25.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 4:30 AM

Last edited

—

Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

1,207

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

5,650 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

1,207 tok/s

Prefill

53.5 tok/s

Total

2,585 tok/s

TTFT

5,650 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

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

64

Scheduler delay

—

Num parallel

—

Concurrency

64

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-vq36-35b-fp8-mtp2-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-35b-fp8-mtp2-ns64 --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); 1207.4 tok/s is the aggregate decode rate across all 64 streams, about 18.9 tok/s per stream, and the 5650 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 64, 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 64: 1197.3, 1207.4, 1250.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:20 AM

Last edited

—

Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

511

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

201 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

511 tok/s

Prefill

1,505 tok/s

Total

1,023 tok/s

TTFT

201 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

302

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

Scheduler delay

—

Num parallel

—

Concurrency

4

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-vq36-35b-fp8-mtp2-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-35b-fp8-mtp2-ns64 --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); 511.1 tok/s is the aggregate decode rate across all 4 streams, about 127.8 tok/s per stream, and the 201 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 64, max_model_len 32768; 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 4: 513.5, 511.1, 516.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-mtp2-ns64, 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, 4:20 AM

Last edited

—

557

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

282 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

557 tok/s

Prefill

1,048 tok/s

Total

1,173 tok/s

TTFT

282 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

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-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-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); 557 tok/s is the aggregate decode rate across all 8 streams, about 69.6 tok/s per stream, and the 282 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 8: 526.6, 582.7, 557 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:20 AM

Last edited

—

2,490

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

4,853 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

2,490 tok/s

Prefill

60.9 tok/s

Total

5,344 tok/s

TTFT

4,853 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 2490.4 tok/s is the aggregate decode rate across all 256 streams, about 9.7 tok/s per stream, and the 4853 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 2491.9, 2482.3, 2490.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:20 AM

Last edited

—

19.3

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q2_K

TTFT

4,568 ms

Context

2k · today

Show all run details

Model

XiaomiMiMo/MiMo-V2.6-Flash-RL

Display name

MiMo-V2.6-Flash-RL

Base model

—

Revision

f1ef9bfdc3706af3f807752765e52b885e113aae

Family

—

Parameters

311B

Active params

—

MoE

yes

Output

19.3 tok/s

Prefill

65.0 tok/s

Total

31.1 tok/s

TTFT

4,568 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

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

Q2_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-mimo26f-q2k-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/MiMo-V2.6-Flash/MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mimo26f-q2k-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 126 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47318 MiB of VRAM in use after load; a model download was running on the VM during this run, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/MiMo-V2.6-Flash-RL-GGUF @ f1ef9bfdc3706af3f807752765e52b885e113aae, file MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 18.8, 21.8, 19.3 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, 4:19 AM

Last edited

—

1,626

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

1,218 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,626 tok/s

Prefill

244 tok/s

Total

3,457 tok/s

TTFT

1,218 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-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-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); 1625.5 tok/s is the aggregate decode rate across all 64 streams, about 25.4 tok/s per stream, and the 1218 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: 1636.5, 1625.5, 1621.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:09 AM

Last edited

—

1,186

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

734 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,186 tok/s

Prefill

404 tok/s

Total

2,517 tok/s

TTFT

734 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

32

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 1186.2 tok/s is the aggregate decode rate across all 32 streams, about 37.1 tok/s per stream, and the 734 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 32: 1198.6, 1186.2, 1198.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-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, 4:09 AM

Last edited

—

2,132

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

2,397 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

2,132 tok/s

Prefill

123 tok/s

Total

4,538 tok/s

TTFT

2,397 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

128

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

128

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 2131.9 tok/s is the aggregate decode rate across all 128 streams, about 16.7 tok/s per stream, and the 2397 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 2150.7, 2129.6, 2131.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:09 AM

Last edited

—

127

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

64.2 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

127 tok/s

Prefill

4,625 tok/s

Total

266 tok/s

TTFT

64.2 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Config vq3-30b-2507-fp8, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 127.2, 126.6, 126 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, 4:09 AM

Last edited

—

29.7

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q4_K_M

TTFT

3,729 ms

Context

2k · today

Show all run details

Model

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

Display name

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

Base model

Llama-4-Scout-17B-16E

Revision

91d32823a1855f7ca12919dffa86701254271eb9

Family

Llama

Parameters

109B

Active params

17B

MoE

yes

Output

29.7 tok/s

Prefill

73.8 tok/s

Total

58.4 tok/s

TTFT

3,729 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

275

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

Q4_K_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-l4s-q4km-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/Llama-4-Scout/meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M/meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-l4s-q4km-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); 29.7 tok/s is the aggregate decode rate across all 8 streams, about 3.7 tok/s per stream, and the 3729 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 67 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load; a model download was running on the VM during this run, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF @ 91d32823a1855f7ca12919dffa86701254271eb9, file meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 29.7, 35.1, 30.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 4:09 AM

Last edited

—

Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

1,167

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

893 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

1,167 tok/s

Prefill

340 tok/s

Total

2,481 tok/s

TTFT

893 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

Scheduler delay

—

Num parallel

—

Concurrency

32

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-vq36-35b-fp8-mtp2-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-35b-fp8-mtp2-ns64 --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); 1166.7 tok/s is the aggregate decode rate across all 32 streams, about 36.5 tok/s per stream, and the 893 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 64, 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 32: 1193.4, 1142.6, 1166.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 3:59 AM

Last edited

—

ModelHardwareEnginetok/s outprefilltok/s totalTTFTDepthShare
Qwen3.6-27B-FP8

27B · Qwen

RTX A6000 48GB
vllmFP823.61,20350.7252 ms0k
Show all details for Qwen3.6-27B-FP8

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

23.6 tok/s

Prefill

1,203 tok/s

Total

50.7 tok/s

TTFT

252 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

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

64

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-vq36-27b-fp8-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq36-27b-fp8-ns64 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Config vq36-27b-fp8-ns64, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 23.6, 23.6, 23.6 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, 4:50 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

RTX A6000 48GB
vllmFP83121905911,612 ms0k
Show all details for Qwen3.6-27B-FP8

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

312 tok/s

Prefill

190 tok/s

Total

591 tok/s

TTFT

1,612 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

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

64

Scheduler delay

—

Num parallel

—

Concurrency

8

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --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); 312 tok/s is the aggregate decode rate across all 8 streams, about 39.0 tok/s per stream, and the 1612 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 64, 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: 314.3, 312, 309.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, 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, 4:50 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

RTX A6000 48GB
vllmFP835716.076419,044 ms0k
Show all details for Qwen3.6-27B-FP8

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

357 tok/s

Prefill

16.0 tok/s

Total

764 tok/s

TTFT

19,044 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

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

64

Scheduler delay

—

Num parallel

—

Concurrency

64

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --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); 357.1 tok/s is the aggregate decode rate across all 64 streams, about 5.6 tok/s per stream, and the 19044 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 64, 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: 357.6, 357.1, 356.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:50 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

RTX A6000 48GB
vllmFP8183340358897 ms0k
Show all details for Qwen3.6-27B-FP8

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

183 tok/s

Prefill

340 tok/s

Total

358 tok/s

TTFT

897 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

Scheduler delay

—

Num parallel

—

Concurrency

4

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --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); 183.2 tok/s is the aggregate decode rate across all 4 streams, about 45.8 tok/s per stream, and the 897 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 64, 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 4: 183.2, 184.9, 183 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:50 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

RTX A6000 48GB
vllmFP835469.17434,429 ms0k
Show all details for Qwen3.6-27B-FP8

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

354 tok/s

Prefill

69.1 tok/s

Total

743 tok/s

TTFT

4,429 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

Scheduler delay

—

Num parallel

—

Concurrency

32

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --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); 354.4 tok/s is the aggregate decode rate across all 32 streams, about 11.1 tok/s per stream, and the 4429 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 64, 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 32: 354.2, 354.4, 354.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 4:40 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

RTX A6000 48GB
vllmFP898.7716202437 ms0k
Show all details for Qwen3.6-27B-FP8

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

98.7 tok/s

Prefill

716 tok/s

Total

202 tok/s

TTFT

437 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

304

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

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

64

Scheduler delay

—

Num parallel

—

Concurrency

2

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 98.7 tok/s is the aggregate decode rate across all 2 streams, about 49.4 tok/s per stream, and the 437 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 64, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (304 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 2: 98.8, 99.8, 98.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, 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, 4:40 AM

Last edited

—

Qwen3.6-27B-FP8

27B · Qwen

RTX A6000 48GB
vllmFP844498.28723,111 ms0k
Show all details for Qwen3.6-27B-FP8

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

444 tok/s

Prefill

98.2 tok/s

Total

872 tok/s

TTFT

3,111 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

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

64

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --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); 443.9 tok/s is the aggregate decode rate across all 16 streams, about 27.7 tok/s per stream, and the 3111 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 64, 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: 443.9, 446.5, 446.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-27b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

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Submitted

Oct 27, 2026, 4:40 AM

Last edited

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Qwen3.6-27B-FP8

27B · Qwen

RTX A6000 48GB
vllmFP851.11,208107252 ms0k
Show all details for Qwen3.6-27B-FP8

Model

Qwen/Qwen3.6-27B-FP8

Display name

Qwen3.6-27B-FP8

Base model

Qwen3.6-27B

Revision

e89b16ebf1988b3d6befa7de50abc2d76f26eb09

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

51.1 tok/s

Prefill

1,208 tok/s

Total

107 tok/s

TTFT

252 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

Scheduler delay

—

Num parallel

—

Concurrency

1

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-vq36-27b-fp8-mtp2-ns64 --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/Qwen3.6-27B-FP8 --served-model-name Qwen3.6-27B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-27b-fp8-mtp2-ns64 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Config vq36-27b-fp8-mtp2-ns64, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 51.1, 50.5, 52.1 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, 4:40 AM

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MiMo-V2.6-Flash-RL

311B MoE

RTX A6000 48GB
llama.cppQ2_K17.233.334.58,851 ms0k
Show all details for MiMo-V2.6-Flash-RL

Model

XiaomiMiMo/MiMo-V2.6-Flash-RL

Display name

MiMo-V2.6-Flash-RL

Base model

—

Revision

f1ef9bfdc3706af3f807752765e52b885e113aae

Family

—

Parameters

311B

Active params

—

MoE

yes

Output

17.2 tok/s

Prefill

33.3 tok/s

Total

34.5 tok/s

TTFT

8,851 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

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

Q2_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-mimo26f-q2k-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/MiMo-V2.6-Flash/MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mimo26f-q2k-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); 17.2 tok/s is the aggregate decode rate across all 8 streams, about 2.1 tok/s per stream, and the 8851 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 126 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47318 MiB of VRAM in use after load; a model download was running on the VM during this run, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/MiMo-V2.6-Flash-RL-GGUF @ f1ef9bfdc3706af3f807752765e52b885e113aae, file MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 13.5, 17.2, 19.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

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Oct 27, 2026, 4:40 AM

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Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP82061,529419200 ms0k
Show all details for Qwen3.6-35B-A3B-FP8

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

206 tok/s

Prefill

1,529 tok/s

Total

419 tok/s

TTFT

200 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

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

64

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-vq36-35b-fp8-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq36-35b-fp8-ns64 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (batch 2, lmx --concurrency 2); 206.3 tok/s is the aggregate decode rate across all 2 streams, about 103.2 tok/s per stream, and the 200 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 64, 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 2: 206.3, 207.3, 207.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing. Selection declared before measuring: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional mean, which LocalMaxxing refuses.

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Submitted

Oct 27, 2026, 4:30 AM

Last edited

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Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP88904571,822668 ms0k
Show all details for Qwen3.6-35B-A3B-FP8

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

890 tok/s

Prefill

457 tok/s

Total

1,822 tok/s

TTFT

668 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

16

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

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-vq36-35b-fp8-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq36-35b-fp8-ns64 --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); 890.4 tok/s is the aggregate decode rate across all 16 streams, about 55.6 tok/s per stream, and the 668 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 64, 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 16: 872.7, 895.5, 890.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-ns64, 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, 4:30 AM

Last edited

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Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81262,742263111 ms0k
Show all details for Qwen3.6-35B-A3B-FP8

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

126 tok/s

Prefill

2,742 tok/s

Total

263 tok/s

TTFT

111 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

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-vq36-35b-fp8-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-vq36-35b-fp8-ns64 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768 --limit-mm-per-prompt {"image":0,"video":0}

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Config vq36-35b-fp8-ns64, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 126.6, 126.2, 125.8 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, 4:30 AM

Last edited

—

Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP88559561,726318 ms0k
Show all details for Qwen3.6-35B-A3B-FP8

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

855 tok/s

Prefill

956 tok/s

Total

1,726 tok/s

TTFT

318 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

303

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

64

Scheduler delay

—

Num parallel

—

Concurrency

8

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-vq36-35b-fp8-mtp2-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-35b-fp8-mtp2-ns64 --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); 855.4 tok/s is the aggregate decode rate across all 8 streams, about 106.9 tok/s per stream, and the 318 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 64, 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 8: 855.4, 818.6, 800.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-mtp2-ns64, 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, 4:30 AM

Last edited

—

MiMo-V2.6-Flash-RL

311B MoE

RTX A6000 48GB
llama.cppQ2_K25.256.648.05,212 ms0k
Show all details for MiMo-V2.6-Flash-RL

Model

XiaomiMiMo/MiMo-V2.6-Flash-RL

Display name

MiMo-V2.6-Flash-RL

Base model

—

Revision

f1ef9bfdc3706af3f807752765e52b885e113aae

Family

—

Parameters

311B

Active params

—

MoE

yes

Output

25.2 tok/s

Prefill

56.6 tok/s

Total

48.0 tok/s

TTFT

5,212 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

295

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

Q2_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-mimo26f-q2k-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/MiMo-V2.6-Flash/MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mimo26f-q2k-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); 25.2 tok/s is the aggregate decode rate across all 4 streams, about 6.3 tok/s per stream, and the 5212 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 126 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47318 MiB of VRAM in use after load; a model download was running on the VM during this run, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/MiMo-V2.6-Flash-RL-GGUF @ f1ef9bfdc3706af3f807752765e52b885e113aae, file MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 15.7, 15.9, 25.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 4:30 AM

Last edited

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Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,20753.52,5855,650 ms0k
Show all details for Qwen3.6-35B-A3B-FP8

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

1,207 tok/s

Prefill

53.5 tok/s

Total

2,585 tok/s

TTFT

5,650 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

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

64

Scheduler delay

—

Num parallel

—

Concurrency

64

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-vq36-35b-fp8-mtp2-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-35b-fp8-mtp2-ns64 --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); 1207.4 tok/s is the aggregate decode rate across all 64 streams, about 18.9 tok/s per stream, and the 5650 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 64, 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 64: 1197.3, 1207.4, 1250.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 4:20 AM

Last edited

—

Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP85111,5051,023201 ms0k
Show all details for Qwen3.6-35B-A3B-FP8

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

511 tok/s

Prefill

1,505 tok/s

Total

1,023 tok/s

TTFT

201 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

302

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

Scheduler delay

—

Num parallel

—

Concurrency

4

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-vq36-35b-fp8-mtp2-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-35b-fp8-mtp2-ns64 --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); 511.1 tok/s is the aggregate decode rate across all 4 streams, about 127.8 tok/s per stream, and the 201 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 64, max_model_len 32768; 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 4: 513.5, 511.1, 516.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-mtp2-ns64, 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, 4:20 AM

Last edited

—

Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP85571,0481,173282 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

557 tok/s

Prefill

1,048 tok/s

Total

1,173 tok/s

TTFT

282 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

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-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-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); 557 tok/s is the aggregate decode rate across all 8 streams, about 69.6 tok/s per stream, and the 282 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 8: 526.6, 582.7, 557 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 4:20 AM

Last edited

—

Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP82,49060.95,3444,853 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

2,490 tok/s

Prefill

60.9 tok/s

Total

5,344 tok/s

TTFT

4,853 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

256

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

256

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 256 requests sent at once (batch 256, lmx --concurrency 256); 2490.4 tok/s is the aggregate decode rate across all 256 streams, about 9.7 tok/s per stream, and the 4853 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 256: 2491.9, 2482.3, 2490.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 4:20 AM

Last edited

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MiMo-V2.6-Flash-RL

311B MoE

RTX A6000 48GB
llama.cppQ2_K19.365.031.14,568 ms0k
Show all details for MiMo-V2.6-Flash-RL

Model

XiaomiMiMo/MiMo-V2.6-Flash-RL

Display name

MiMo-V2.6-Flash-RL

Base model

—

Revision

f1ef9bfdc3706af3f807752765e52b885e113aae

Family

—

Parameters

311B

Active params

—

MoE

yes

Output

19.3 tok/s

Prefill

65.0 tok/s

Total

31.1 tok/s

TTFT

4,568 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

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

Q2_K

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-mimo26f-q2k-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/MiMo-V2.6-Flash/MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-mimo26f-q2k-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 126 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47318 MiB of VRAM in use after load; a model download was running on the VM during this run, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/MiMo-V2.6-Flash-RL-GGUF @ f1ef9bfdc3706af3f807752765e52b885e113aae, file MiMo-V2.6-Flash-RL-Q2_K-00001-of-00002.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 18.8, 21.8, 19.3 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.

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Submitted

Oct 27, 2026, 4:19 AM

Last edited

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Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,6262443,4571,218 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,626 tok/s

Prefill

244 tok/s

Total

3,457 tok/s

TTFT

1,218 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-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-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); 1625.5 tok/s is the aggregate decode rate across all 64 streams, about 25.4 tok/s per stream, and the 1218 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: 1636.5, 1625.5, 1621.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

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Submitted

Oct 27, 2026, 4:09 AM

Last edited

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Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,1864042,517734 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

1,186 tok/s

Prefill

404 tok/s

Total

2,517 tok/s

TTFT

734 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

32

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 32 requests sent at once (batch 32, lmx --concurrency 32); 1186.2 tok/s is the aggregate decode rate across all 32 streams, about 37.1 tok/s per stream, and the 734 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 32: 1198.6, 1186.2, 1198.4 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-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, 4:09 AM

Last edited

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Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP82,1321234,5382,397 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

2,132 tok/s

Prefill

123 tok/s

Total

4,538 tok/s

TTFT

2,397 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

296

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

128

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

128

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 128 requests sent at once (batch 128, lmx --concurrency 128); 2131.9 tok/s is the aggregate decode rate across all 128 streams, about 16.7 tok/s per stream, and the 2397 ms time to first token is under that load. Setup: KASA cuda-vm, 1 x NVIDIA RTX A6000 48 GB passed through to a VM (28 vCPU of an AMD EPYC 7343, 128 GiB), one card; vLLM 0.32.0 (vllm/vllm-openai@sha256:befccf0e7e4089aa23926bb6f887a85c0d46c2a99e6de5c5ffdf5cb7957c5b34); max_num_seqs ?, max_model_len 32768; speculative decoding off. Each request: LocalMaxxing's canonical reasoning-v1 prompt (296 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 128: 2150.7, 2129.6, 2131.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq3-30b-2507-fp8, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 4:09 AM

Last edited

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Qwen3-30B-A3B-Instruct-2507-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81274,62526664.2 ms0k
Show all details for Qwen3-30B-A3B-Instruct-2507-FP8

Model

Qwen/Qwen3-30B-A3B-Instruct-2507-FP8

Display name

Qwen3-30B-A3B-Instruct-2507-FP8

Base model

Qwen3-30B-A3B-Instruct-2507

Revision

5a5a776300a41aaa681dd7ff0106608ef2bc90db

Family

Qwen

Parameters

30B

Active params

3B

MoE

yes

Output

127 tok/s

Prefill

4,625 tok/s

Total

266 tok/s

TTFT

64.2 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

297

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

—

Scheduler delay

—

Num parallel

—

Concurrency

1

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-vq3-30b-2507-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/Qwen3-30B-A3B-2507-FP8 --served-model-name Qwen3-30B-A3B-2507-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768

Extra flags

--d --name lsv-vq3-30b-2507-fp8 --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --max-model-len 32768

Notes

KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. Config vq3-30b-2507-fp8, vLLM 0.32.0. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 127.2, 126.6, 126 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, 4:09 AM

Last edited

—

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

17B MoE · Llama

RTX A6000 48GB
llama.cppQ4_K_M29.773.858.43,729 ms0k
Show all details for Llama-4-Scout-17B-16E-Instruct

Model

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

Display name

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

Base model

Llama-4-Scout-17B-16E

Revision

91d32823a1855f7ca12919dffa86701254271eb9

Family

Llama

Parameters

109B

Active params

17B

MoE

yes

Output

29.7 tok/s

Prefill

73.8 tok/s

Total

58.4 tok/s

TTFT

3,729 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

275

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

Q4_K_M

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

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-l4s-q4km-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/Llama-4-Scout/meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M/meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-l4s-q4km-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); 29.7 tok/s is the aggregate decode rate across all 8 streams, about 3.7 tok/s per stream, and the 3729 ms time to first token is under that load. KASA cuda-vm: 1 x NVIDIA RTX A6000 48 GB (PCIe passthrough, driver 615.71.10) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11889 (CUDA image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 67 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47376 MiB of VRAM in use after load; a model download was running on the VM during this run, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF bartowski/meta-llama_Llama-4-Scout-17B-16E-Instruct-old-GGUF @ 91d32823a1855f7ca12919dffa86701254271eb9, file meta-llama_Llama-4-Scout-17B-16E-Instruct-Q4_K_M-00001-of-00002.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 29.7, 35.1, 30.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

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Submitted

Oct 27, 2026, 4:09 AM

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Qwen3.6-35B-A3B-FP8

3B MoE · Qwen

RTX A6000 48GB
vllmFP81,1673402,481893 ms0k
Show all details for Qwen3.6-35B-A3B-FP8

Model

Qwen/Qwen3.6-35B-A3B-FP8

Display name

Qwen3.6-35B-A3B-FP8

Base model

Qwen3.6-35B-A3B

Revision

95a723d08a9490559dae23d0cff1d9466213d989

Family

Qwen

Parameters

35B

Active params

3B

MoE

yes

Output

1,167 tok/s

Prefill

340 tok/s

Total

2,481 tok/s

TTFT

893 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

305

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

32

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

FP8

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

—

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

0.92

Max running seqs

64

Scheduler delay

—

Num parallel

—

Concurrency

32

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-vq36-35b-fp8-mtp2-ns64 --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/Qwen3.6-35B-A3B-FP8 --served-model-name Qwen3.6-35B-A3B-FP8 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-num-seqs 64 --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-vq36-35b-fp8-mtp2-ns64 --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); 1166.7 tok/s is the aggregate decode rate across all 32 streams, about 36.5 tok/s per stream, and the 893 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 64, 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 32: 1193.4, 1142.6, 1166.7 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config vq36-35b-fp8-mtp2-ns64, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

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Submitted

Oct 27, 2026, 3:59 AM

Last edited

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