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imthekaiser

imthekaiser

Verified

@imthekaiser · Member since 2026

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Verified 1w ago Mainly llama.cpp 200 approved runs

Submissions

1,179

200 approved public runs

Models

28

unique models

Avg output

294tok/s

Best 3,216 tok/s

Avg prefill

313tok/s

Best 2,995 tok/s

Avg total

608tok/s

Best 6,953 tok/s

Avg TTFT

5,636ms

Best 110 ms

Hardware

2

distinct setups

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 bests28

Fastest approved result for each model.

20B MoE6 runs
Output3,216 tok/s
Prefill185 tok/s
TTFT1,794 ms
Depth0k
RTX A6000 48GB

vllm · MXFP4 · today

3B MoE6 runs
Output1,901 tok/s
Prefill163 tok/s
TTFT1,823 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

3B MoE6 runs
Output1,820 tok/s
Prefill167 tok/s
TTFT1,769 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

9B15 runs
Output1,746 tok/s
Prefill44.6 tok/s
TTFT6,884 ms
Depth0k
RTX A6000 48GB

vllm · BF16 · today

3B MoE5 runs
Output1,444 tok/s
Prefill298 tok/s
TTFT1,031 ms
Depth0k
RTX A6000 48GB

vllm · INT4_W4A16 · today

3B MoE1 run
Output1,045 tok/s
Prefill475 tok/s
TTFT638 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

4B MoE7 runs
Output675 tok/s
Prefill68.7 tok/s
TTFT4,471 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-IQ4_XS · today

32B6 runs
Output567 tok/s
Prefill53.1 tok/s
TTFT5,550 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

27B12 runs
Output533 tok/s
Prefill62.3 tok/s
TTFT5,519 ms
Depth0k
RTX A6000 48GB

vllm · INT4_W4A16 · today

31B MoE7 runs
Output533 tok/s
Prefill115 tok/s
TTFT2,428 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q4_K_XL · today

27B5 runs
Output437 tok/s
Prefill52.2 tok/s
TTFT6,621 ms
Depth0k
RTX A6000 48GB

vllm · FP8 · today

80B MoE7 runs
Output406 tok/s
Prefill127 tok/s
TTFT2,335 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q3_K_XL · today

31B5 runs
Output264 tok/s
Prefill18.7 tok/s
TTFT16,397 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q4_K_XL · today

122B MoE6 runs
Output233 tok/s
Prefill54.7 tok/s
TTFT5,549 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

180B26 runs
Output231 tok/s
Prefill5.8 tok/s
TTFT59,930 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

70B4 runs
Output228 tok/s
Prefill46.0 tok/s
TTFT6,614 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

10B MoE12 runs
Output224 tok/s
Prefill58.8 tok/s
TTFT5,156 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

70B4 runs
Output222 tok/s
Prefill47.5 tok/s
TTFT6,360 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

72B4 runs
Output162 tok/s
Prefill48.4 tok/s
TTFT6,530 ms
Depth0k
RTX A6000 48GB

vllm · AWQ · today

27B20 runs
Output143 tok/s
Prefill33.2 tok/s
TTFT10,465 ms
Depth0k
Intel Arc Pro B65 32GB

llama.cpp · UD-Q4_K_XL · today

120B MoE5 runs
Output115 tok/s
Prefill20.7 tok/s
TTFT16,236 ms
Depth0k
RTX A6000 48GB

llama.cpp · MXFP4 · today

3B MoE4 runs
Output101 tok/s
Prefill74.2 tok/s
TTFT4,106 ms
Depth0k
Intel Arc Pro B65 32GB

llama.cpp · Q4_0 · today

17B MoE4 runs
Output73.8 tok/s
Prefill39.8 tok/s
TTFT6,929 ms
Depth0k
RTX A6000 48GB

llama.cpp · IQ4_XS · today

304B MoE7 runs
Output54.7 tok/s
Prefill8.5 tok/s
TTFT32,583 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

199B9 runs
Output30.4 tok/s
Prefill97.2 tok/s
TTFT2,892 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q4_K · today

12B MoE5 runs
Output28.4 tok/s
Prefill17.6 tok/s
TTFT17,421 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q4_0 · today

321B1 run
Output16.0 tok/s
Prefill11.7 tok/s
TTFT24,401 ms
Depth0k
RTX A6000 48GB

llama.cpp · UD-Q2_K_XL · today

311B MoE1 run
Output12.5 tok/s
Prefill9.2 tok/s
TTFT32,129 ms
Depth0k
RTX A6000 48GB

llama.cpp · Q2_K · today

Hardware breakdown

Hardware breakdown2

Distinct rigs used across approved submissions.

RTX A6000 48GB

155 runs · best 3,216 tok/s

Intel Arc Pro B65 32GB

45 runs · best 364 tok/s

Engines used

Engines used3

Runtime mix and top quantizations.

llama.cpp118Q8_0 · MXFP4 · UD-Q6_K
vllm74INT4_W4A16 · FP8 · AWQ
strata8IQ3_S
All runs

All runs200

Full approved speed test history · page 1 of 8.

Qwen3.8-27B

27B · Qwen

80.0

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · Q8_0

TTFT

9,456 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

80.0 tok/s

Prefill

36.4 tok/s

Total

137 tok/s

TTFT

9,456 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

Q8_0

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q8-0-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-Q8_0.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q8-0-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 80 tok/s is the aggregate decode rate across all 8 streams, about 10.0 tok/s per stream, and the 9456 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-Q8_0.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 80, 80.7, 80 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 28, 2026, 12:07 AM

Last edited

—

Qwen3.8-27B

27B · Qwen

53.0

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · Q8_0

TTFT

4,009 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

53.0 tok/s

Prefill

86.1 tok/s

Total

103 tok/s

TTFT

4,009 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

Q8_0

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q8-0-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-Q8_0.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q8-0-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 53 tok/s is the aggregate decode rate across all 4 streams, about 13.2 tok/s per stream, and the 4009 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-Q8_0.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: 53.7, 53, 52.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 28, 2026, 12:07 AM

Last edited

—

Qwen3.8-27B

27B · Qwen

30.8

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · Q8_0

TTFT

2,012 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

30.8 tok/s

Prefill

172 tok/s

Total

64.5 tok/s

TTFT

2,012 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

Q8_0

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q27-q8-0-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-Q8_0.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q8-0-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 30.8 tok/s is the aggregate decode rate across all 2 streams, about 15.4 tok/s per stream, and the 2012 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-Q8_0.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 31, 31, 30.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 28, 2026, 12:07 AM

Last edited

—

gpt-oss-120b

120B MoE · Gpt

17.1

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · MXFP4

TTFT

3,456 ms

Context

2k · today

Show all run details

Model

openai/gpt-oss-120b

Display name

gpt-oss-120b

Base model

—

Revision

7200b74bea78b0a2964ef9f3897eefa401e890ff

Family

Gpt

Parameters

120B

Active params

—

MoE

yes

Output

17.1 tok/s

Prefill

96.7 tok/s

Total

35.4 tok/s

TTFT

3,456 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

334

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

MXFP4

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-oss120-mxfp4-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/gpt-oss-120b/gpt-oss-120b-MXFP4.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-oss120-mxfp4-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 17.1 tok/s is the aggregate decode rate across all 2 streams, about 8.6 tok/s per stream, and the 3456 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 63 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/gpt-oss-120b-GGUF @ 7200b74bea78b0a2964ef9f3897eefa401e890ff, file gpt-oss-120b-MXFP4.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 17.1, 18.3, 3.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 28, 2026, 12:07 AM

Last edited

—

gpt-oss-120b

120B MoE · Gpt

15.0

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · MXFP4

TTFT

4,234 ms

Context

2k · today

Show all run details

Model

openai/gpt-oss-120b

Display name

gpt-oss-120b

Base model

—

Revision

7200b74bea78b0a2964ef9f3897eefa401e890ff

Family

Gpt

Parameters

120B

Active params

—

MoE

yes

Output

15.0 tok/s

Prefill

79.1 tok/s

Total

27.8 tok/s

TTFT

4,234 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

335

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

MXFP4

Backend

xpu

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-oss120-mxfp4-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/gpt-oss-120b/gpt-oss-120b-MXFP4.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-oss120-mxfp4-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 63 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/gpt-oss-120b-GGUF @ 7200b74bea78b0a2964ef9f3897eefa401e890ff, file gpt-oss-120b-MXFP4.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 38.5, 15, 12.8 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 28, 2026, 12:07 AM

Last edited

—

Qwen3.8-27B

27B · Qwen

15.9

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · Q8_0

TTFT

818 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

15.9 tok/s

Prefill

419 tok/s

Total

35.6 tok/s

TTFT

818 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

343

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

Q8_0

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q8-0-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-Q8_0.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q8-0-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-Q8_0.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 16, 15.9, 15.9 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, 11:46 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

78.8

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q6_K

TTFT

9,199 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

78.8 tok/s

Prefill

37.6 tok/s

Total

137 tok/s

TTFT

9,199 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q6_K

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q6-k-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q6_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q6-k-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 78.8 tok/s is the aggregate decode rate across all 8 streams, about 9.8 tok/s per stream, and the 9199 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q6_K.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: 78.8, 78.7, 78.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:46 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

55.2

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q6_K

TTFT

4,479 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

55.2 tok/s

Prefill

77.2 tok/s

Total

105 tok/s

TTFT

4,479 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q6_K

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q6-k-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q6_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q6-k-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 55.2 tok/s is the aggregate decode rate across all 4 streams, about 13.8 tok/s per stream, and the 4479 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q6_K.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: 55.2, 55.2, 55 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 27, 2026, 11:46 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

33.9

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q6_K

TTFT

2,497 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

33.9 tok/s

Prefill

139 tok/s

Total

68.4 tok/s

TTFT

2,497 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q6_K

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q27-q6-k-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q6_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q6-k-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 33.9 tok/s is the aggregate decode rate across all 2 streams, about 16.9 tok/s per stream, and the 2497 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q6_K.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: 33.9, 34, 34 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:46 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

17.5

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q6_K

TTFT

1,187 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

17.5 tok/s

Prefill

289 tok/s

Total

38.1 tok/s

TTFT

1,187 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

343

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q6_K

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q6-k-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q6_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q6-k-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q6_K.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 17.5, 17.5, 17.6 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

Reactions

—

Submitted

Oct 27, 2026, 11:46 PM

Last edited

—

25.6

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q4_K

TTFT

3,804 ms

Context

2k · today

Show all run details

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

25.6 tok/s

Prefill

74.1 tok/s

Total

49.2 tok/s

TTFT

3,804 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

282

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

Q4_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

4

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-step35f-q4k-np4-quiet --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/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-step35f-q4k-np4-quiet --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); 25.6 tok/s is the aggregate decode rate across all 4 streams, about 6.4 tok/s per stream, and the 3804 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 118 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; 47272 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.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: 26.9, 27.9, 25.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:40 PM

Last edited

—

22.0

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q4_K

TTFT

2,956 ms

Context

2k · today

Show all run details

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

22.0 tok/s

Prefill

95.4 tok/s

Total

41.0 tok/s

TTFT

2,956 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

282

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q4_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

4

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-step35f-q4k-np4-quiet --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/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-step35f-q4k-np4-quiet --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 22 tok/s is the aggregate decode rate across all 2 streams, about 11.0 tok/s per stream, and the 2956 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 118 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; 47272 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 17.7, 20, 22 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:40 PM

Last edited

—

30.4

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · Q4_K

TTFT

2,892 ms

Context

2k · today

Show all run details

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

30.4 tok/s

Prefill

97.2 tok/s

Total

47.6 tok/s

TTFT

2,892 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

281

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

Q4_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

4

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-step35f-q4k-np4-quiet --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/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-step35f-q4k-np4-quiet --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 118 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; 47272 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 30.8, 30.4, 29.5 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, 11:40 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

61.2

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-Q2_K_XL

TTFT

672 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

61.2 tok/s

Prefill

519 tok/s

Total

125 tok/s

TTFT

672 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

349

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q2_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

1

Concurrency

1

Spec decoding

yes

Spec method

MTP

Spec model

/mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf

Spec draft model

—

Spec tokens

1

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-q2kxl-mtp1-np1 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next/UD-Q2_K_XL/Qwen3.8-Flash-Next-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 1 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none --spec-type draft-mtp -md /mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf --spec-draft-n-max 1

Extra flags

--d --name lsv-fn-q2kxl-mtp1-np1 --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 78 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; 47596 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 1 slot, context 32768. MTP speculative decoding from mtp-Qwen3.8-Flash-Next-Q8_0.gguf, up to 1 draft tokens. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-Q2_K_XL-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 55.3, 61.2, 66.9 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, 11:40 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

40.3

tok/s

Hardware

RTX A6000 48GB

Engine

llama.cpp · UD-IQ3_XXS

TTFT

891 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

40.3 tok/s

Prefill

387 tok/s

Total

83.3 tok/s

TTFT

891 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ3_XXS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

1

Concurrency

1

Spec decoding

yes

Spec method

MTP

Spec model

/mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf

Spec draft model

—

Spec tokens

1

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-iq3xxs-mtp1-np1 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next-IQ3/UD-IQ3_XXS/Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 1 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none --spec-type draft-mtp -md /mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf --spec-draft-n-max 1

Extra flags

--d --name lsv-fn-iq3xxs-mtp1-np1 --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 81 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47522 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 1 slot, context 32768. MTP speculative decoding from mtp-Qwen3.8-Flash-Next-Q8_0.gguf, up to 1 draft tokens. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 42.5, 39.3, 40.3 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, 11:40 PM

Last edited

—

Qwen3.8-27B-INT4

27B · Qwen

533

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · INT4_W4A16

TTFT

5,519 ms

Context

33k · today

Show all run details

Model

RedHatAI/Qwen3.8-27B-INT4

Display name

Qwen3.8-27B-INT4

Base model

Qwen3.8-27B

Revision

91bd022d5b49442a868bc35008f6c21e1860edfa

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

533 tok/s

Prefill

62.3 tok/s

Total

1,215 tok/s

TTFT

5,519 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

48

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

INT4_W4A16

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

48

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-v27-int4 --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/Qwen3.8-27B-INT4 --served-model-name Qwen3.8-27B-INT4 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-v27-int4 --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. 48 requests sent at once (batch 48, lmx --concurrency 48); 533.4 tok/s is the aggregate decode rate across all 48 streams, about 11.1 tok/s per stream, and the 5519 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 (346 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 48: 533.6, 533.4, 533.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-int4, 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, 11:30 PM

Last edited

—

Qwen3.8-27B-INT4

27B · Qwen

106

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · INT4_W4A16

TTFT

607 ms

Context

33k · today

Show all run details

Model

RedHatAI/Qwen3.8-27B-INT4

Display name

Qwen3.8-27B-INT4

Base model

Qwen3.8-27B

Revision

91bd022d5b49442a868bc35008f6c21e1860edfa

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

106 tok/s

Prefill

574 tok/s

Total

236 tok/s

TTFT

607 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

3

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

INT4_W4A16

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

3

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-v27-int4 --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/Qwen3.8-27B-INT4 --served-model-name Qwen3.8-27B-INT4 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-v27-int4 --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. 3 requests sent at once (batch 3, lmx --concurrency 3); 105.8 tok/s is the aggregate decode rate across all 3 streams, about 35.3 tok/s per stream, and the 607 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 (347 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 3: 105.9, 105.8, 105.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-int4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 11:30 PM

Last edited

—

Qwen3.8-27B-INT4

27B · Qwen

442

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · INT4_W4A16

TTFT

3,603 ms

Context

33k · today

Show all run details

Model

RedHatAI/Qwen3.8-27B-INT4

Display name

Qwen3.8-27B-INT4

Base model

Qwen3.8-27B

Revision

91bd022d5b49442a868bc35008f6c21e1860edfa

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

442 tok/s

Prefill

96.6 tok/s

Total

985 tok/s

TTFT

3,603 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

24

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

INT4_W4A16

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

24

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-v27-int4 --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/Qwen3.8-27B-INT4 --served-model-name Qwen3.8-27B-INT4 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-v27-int4 --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. 24 requests sent at once (batch 24, lmx --concurrency 24); 441.5 tok/s is the aggregate decode rate across all 24 streams, about 18.4 tok/s per stream, and the 3603 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 (346 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 24: 441.5, 441.5, 442 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-int4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 11:30 PM

Last edited

—

Qwen3.8-27B-INT4

27B · Qwen

317

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · INT4_W4A16

TTFT

1,931 ms

Context

33k · today

Show all run details

Model

RedHatAI/Qwen3.8-27B-INT4

Display name

Qwen3.8-27B-INT4

Base model

Qwen3.8-27B

Revision

91bd022d5b49442a868bc35008f6c21e1860edfa

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

317 tok/s

Prefill

183 tok/s

Total

694 tok/s

TTFT

1,931 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

348

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

INT4_W4A16

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

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-v27-int4 --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/Qwen3.8-27B-INT4 --served-model-name Qwen3.8-27B-INT4 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-v27-int4 --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. 12 requests sent at once (batch 12, lmx --concurrency 12); 316.7 tok/s is the aggregate decode rate across all 12 streams, about 26.4 tok/s per stream, and the 1931 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 (348 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 12: 316.9, 316.7, 317.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-int4, 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, 11:30 PM

Last edited

—

Qwen3.8-27B-FP8

27B · Qwen

437

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

6,621 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.8-27B-FP8

Display name

Qwen3.8-27B-FP8

Base model

Qwen3.8-27B

Revision

017b9c7af6b5689d5dd426a76e0bc077eb5ca20a

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

437 tok/s

Prefill

52.2 tok/s

Total

995 tok/s

TTFT

6,621 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

48

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

48

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-v27-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/Qwen3.8-27B-FP8 --served-model-name Qwen3.8-27B-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-v27-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. 48 requests sent at once (batch 48, lmx --concurrency 48); 437.1 tok/s is the aggregate decode rate across all 48 streams, about 9.1 tok/s per stream, and the 6621 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 (346 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 48: 437.1, 436.5, 437.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-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, 11:30 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

70.9

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q5_K_M

TTFT

9,168 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

70.9 tok/s

Prefill

37.8 tok/s

Total

127 tok/s

TTFT

9,168 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q5_K_M

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q5-k-m-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q5_K_M.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q5-k-m-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 70.9 tok/s is the aggregate decode rate across all 8 streams, about 8.9 tok/s per stream, and the 9168 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q5_K_M.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: 72.3, 70.9, 72.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:26 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

33.9

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q5_K_M

TTFT

2,427 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

33.9 tok/s

Prefill

142 tok/s

Total

68.5 tok/s

TTFT

2,427 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q5_K_M

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q27-q5-k-m-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q5_K_M.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q5-k-m-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 33.9 tok/s is the aggregate decode rate across all 2 streams, about 16.9 tok/s per stream, and the 2427 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q5_K_M.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: 34, 33.8, 33.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:26 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

17.8

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q5_K_M

TTFT

1,244 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

17.8 tok/s

Prefill

279 tok/s

Total

38.8 tok/s

TTFT

1,244 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q5_K_M

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q5-k-m-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q5_K_M.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q5-k-m-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q5_K_M.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 17.8, 17.9, 17.8 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, 11:26 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

54.5

tok/s

Hardware

Intel Arc Pro B65 32GB

Engine

llama.cpp · UD-Q4_K_XL

TTFT

9,005 ms

Context

2k · today

Show all run details

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

54.5 tok/s

Prefill

38.5 tok/s

Total

104 tok/s

TTFT

9,005 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

348

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q4_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q4-k-xl-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q4_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q4-k-xl-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 54.5 tok/s is the aggregate decode rate across all 8 streams, about 6.8 tok/s per stream, and the 9005 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q4_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 54.6, 54.6, 54.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:26 PM

Last edited

—

Qwen3.8-27B-FP8

27B · Qwen

324

tok/s

Hardware

RTX A6000 48GB

Engine

vllm · FP8

TTFT

4,335 ms

Context

33k · today

Show all run details

Model

Qwen/Qwen3.8-27B-FP8

Display name

Qwen3.8-27B-FP8

Base model

Qwen3.8-27B

Revision

017b9c7af6b5689d5dd426a76e0bc077eb5ca20a

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

324 tok/s

Prefill

79.7 tok/s

Total

727 tok/s

TTFT

4,335 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

24

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

24

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-v27-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/Qwen3.8-27B-FP8 --served-model-name Qwen3.8-27B-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-v27-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. 24 requests sent at once (batch 24, lmx --concurrency 24); 323.8 tok/s is the aggregate decode rate across all 24 streams, about 13.5 tok/s per stream, and the 4335 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 (346 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 24: 324.2, 323.8, 323.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-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, 11:20 PM

Last edited

—

ModelHardwareEnginetok/s outprefilltok/s totalTTFTDepthShare
Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppQ8_080.036.41379,456 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

80.0 tok/s

Prefill

36.4 tok/s

Total

137 tok/s

TTFT

9,456 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

Q8_0

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q8-0-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-Q8_0.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q8-0-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 80 tok/s is the aggregate decode rate across all 8 streams, about 10.0 tok/s per stream, and the 9456 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-Q8_0.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 80, 80.7, 80 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 28, 2026, 12:07 AM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppQ8_053.086.11034,009 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

53.0 tok/s

Prefill

86.1 tok/s

Total

103 tok/s

TTFT

4,009 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

Q8_0

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q8-0-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-Q8_0.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q8-0-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 53 tok/s is the aggregate decode rate across all 4 streams, about 13.2 tok/s per stream, and the 4009 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-Q8_0.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: 53.7, 53, 52.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 28, 2026, 12:07 AM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppQ8_030.817264.52,012 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

30.8 tok/s

Prefill

172 tok/s

Total

64.5 tok/s

TTFT

2,012 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

Q8_0

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q27-q8-0-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-Q8_0.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q8-0-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 30.8 tok/s is the aggregate decode rate across all 2 streams, about 15.4 tok/s per stream, and the 2012 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-Q8_0.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 31, 31, 30.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 28, 2026, 12:07 AM

Last edited

—

gpt-oss-120b

120B MoE · Gpt

Intel Arc Pro B65 32GB
llama.cppMXFP417.196.735.43,456 ms0k
Show all details for gpt-oss-120b

Model

openai/gpt-oss-120b

Display name

gpt-oss-120b

Base model

—

Revision

7200b74bea78b0a2964ef9f3897eefa401e890ff

Family

Gpt

Parameters

120B

Active params

—

MoE

yes

Output

17.1 tok/s

Prefill

96.7 tok/s

Total

35.4 tok/s

TTFT

3,456 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

334

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

MXFP4

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-oss120-mxfp4-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/gpt-oss-120b/gpt-oss-120b-MXFP4.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-oss120-mxfp4-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 17.1 tok/s is the aggregate decode rate across all 2 streams, about 8.6 tok/s per stream, and the 3456 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 63 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/gpt-oss-120b-GGUF @ 7200b74bea78b0a2964ef9f3897eefa401e890ff, file gpt-oss-120b-MXFP4.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 17.1, 18.3, 3.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 28, 2026, 12:07 AM

Last edited

—

gpt-oss-120b

120B MoE · Gpt

Intel Arc Pro B65 32GB
llama.cppMXFP415.079.127.84,234 ms0k
Show all details for gpt-oss-120b

Model

openai/gpt-oss-120b

Display name

gpt-oss-120b

Base model

—

Revision

7200b74bea78b0a2964ef9f3897eefa401e890ff

Family

Gpt

Parameters

120B

Active params

—

MoE

yes

Output

15.0 tok/s

Prefill

79.1 tok/s

Total

27.8 tok/s

TTFT

4,234 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

335

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

MXFP4

Backend

xpu

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-oss120-mxfp4-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/gpt-oss-120b/gpt-oss-120b-MXFP4.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-oss120-mxfp4-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), placement by llama.cpp's default --fit (1 GiB margin): the 63 GB model does not fit the 32 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; the exact split is not logged at the default verbosity, 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF ggml-org/gpt-oss-120b-GGUF @ 7200b74bea78b0a2964ef9f3897eefa401e890ff, file gpt-oss-120b-MXFP4.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 38.5, 15, 12.8 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 28, 2026, 12:07 AM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppQ8_015.941935.6818 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

15.9 tok/s

Prefill

419 tok/s

Total

35.6 tok/s

TTFT

818 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

343

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

Q8_0

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q8-0-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-Q8_0.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q8-0-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-Q8_0.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 16, 15.9, 15.9 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, 11:46 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppUD-Q6_K78.837.61379,199 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

78.8 tok/s

Prefill

37.6 tok/s

Total

137 tok/s

TTFT

9,199 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q6_K

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q6-k-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q6_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q6-k-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 78.8 tok/s is the aggregate decode rate across all 8 streams, about 9.8 tok/s per stream, and the 9199 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q6_K.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: 78.8, 78.7, 78.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:46 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppUD-Q6_K55.277.21054,479 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

55.2 tok/s

Prefill

77.2 tok/s

Total

105 tok/s

TTFT

4,479 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

4

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q6_K

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q6-k-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q6_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q6-k-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 4 requests sent at once (lmx --concurrency 4); 55.2 tok/s is the aggregate decode rate across all 4 streams, about 13.8 tok/s per stream, and the 4479 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q6_K.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: 55.2, 55.2, 55 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1.

Reactions

—

Submitted

Oct 27, 2026, 11:46 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppUD-Q6_K33.913968.42,497 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

33.9 tok/s

Prefill

139 tok/s

Total

68.4 tok/s

TTFT

2,497 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q6_K

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q27-q6-k-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q6_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q6-k-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 33.9 tok/s is the aggregate decode rate across all 2 streams, about 16.9 tok/s per stream, and the 2497 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q6_K.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: 33.9, 34, 34 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:46 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppUD-Q6_K17.528938.11,187 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

17.5 tok/s

Prefill

289 tok/s

Total

38.1 tok/s

TTFT

1,187 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

343

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q6_K

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q6-k-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q6_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q6-k-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q6_K.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 17.5, 17.5, 17.6 tok/s; this is the median (tie to the earlier). Prompt and output samples were added from the lmx run file, engineTimingsRaw from exact llama-server /metrics deltas around the request.

Reactions

—

Submitted

Oct 27, 2026, 11:46 PM

Last edited

—

Step-3.5-Flash

199B

RTX A6000 48GB
llama.cppQ4_K25.674.149.23,804 ms0k
Show all details for Step-3.5-Flash

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

25.6 tok/s

Prefill

74.1 tok/s

Total

49.2 tok/s

TTFT

3,804 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

282

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

Q4_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

4

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-step35f-q4k-np4-quiet --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/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-step35f-q4k-np4-quiet --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); 25.6 tok/s is the aggregate decode rate across all 4 streams, about 6.4 tok/s per stream, and the 3804 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 118 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; 47272 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.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: 26.9, 27.9, 25.6 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:40 PM

Last edited

—

Step-3.5-Flash

199B

RTX A6000 48GB
llama.cppQ4_K22.095.441.02,956 ms0k
Show all details for Step-3.5-Flash

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

22.0 tok/s

Prefill

95.4 tok/s

Total

41.0 tok/s

TTFT

2,956 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

282

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

Q4_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

4

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-step35f-q4k-np4-quiet --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/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-step35f-q4k-np4-quiet --gpus all --ipc host --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics --b 4096 --lm none

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 22 tok/s is the aggregate decode rate across all 2 streams, about 11.0 tok/s per stream, and the 2956 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 118 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; 47272 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 17.7, 20, 22 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 1 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:40 PM

Last edited

—

Step-3.5-Flash

199B

RTX A6000 48GB
llama.cppQ4_K30.497.247.62,892 ms0k
Show all details for Step-3.5-Flash

Model

stepfun-ai/Step-3.5-Flash

Display name

Step-3.5-Flash

Base model

Step-3.5-Flash

Revision

c439ad64d667632f3e1507757cbe225b6a46e6b8

Family

—

Parameters

199B

Active params

—

MoE

no

Output

30.4 tok/s

Prefill

97.2 tok/s

Total

47.6 tok/s

TTFT

2,892 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

281

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

Q4_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

4

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-step35f-q4k-np4-quiet --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/Step-3.5-Flash/Step-3.5-Flash-Q4_K.gguf --host 0.0.0.0 --port 8000 -fa on -c 16384 -np 4 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none

Extra flags

--d --name lsv-step35f-q4k-np4-quiet --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 118 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; 47272 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 4 server slots of 4096 tokens each, context 16384. GGUF ggml-org/Step-3.5-Flash-GGUF @ c439ad64d667632f3e1507757cbe225b6a46e6b8, file Step-3.5-Flash-Q4_K.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 30.8, 30.4, 29.5 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, 11:40 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

RTX A6000 48GB
llama.cppUD-Q2_K_XL61.2519125672 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

61.2 tok/s

Prefill

519 tok/s

Total

125 tok/s

TTFT

672 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

349

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-Q2_K_XL

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

1

Concurrency

1

Spec decoding

yes

Spec method

MTP

Spec model

/mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf

Spec draft model

—

Spec tokens

1

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-q2kxl-mtp1-np1 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next/UD-Q2_K_XL/Qwen3.8-Flash-Next-UD-Q2_K_XL-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 1 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none --spec-type draft-mtp -md /mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf --spec-draft-n-max 1

Extra flags

--d --name lsv-fn-q2kxl-mtp1-np1 --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 78 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; 47596 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 1 slot, context 32768. MTP speculative decoding from mtp-Qwen3.8-Flash-Next-Q8_0.gguf, up to 1 draft tokens. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-Q2_K_XL-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 55.3, 61.2, 66.9 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, 11:40 PM

Last edited

—

Qwen3.8-Flash-Next

180B · Qwen

RTX A6000 48GB
llama.cppUD-IQ3_XXS40.338783.3891 ms0k
Show all details for Qwen3.8-Flash-Next

Model

Qwen/Qwen3.8-Flash-Next

Display name

Qwen3.8-Flash-Next

Base model

—

Revision

766911a6b7369840a91dbcd95f9f997acaab6cd6

Family

Qwen

Parameters

180B

Active params

—

MoE

no

Output

40.3 tok/s

Prefill

387 tok/s

Total

83.3 tok/s

TTFT

891 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

llama.cpp

Engine version

b11889

Quantization

UD-IQ3_XXS

Backend

cuda

Tensor parallel

—

Pipeline parallel

—

GPU layers

—

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

2048

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

1

Concurrency

1

Spec decoding

yes

Spec method

MTP

Spec model

/mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf

Spec draft model

—

Spec tokens

1

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

yes

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-fn-iq3xxs-mtp1-np1 --gpus all --ipc=host -p 127.0.0.1:8000:8000 -v /mnt/models:/mnt/models -v /mnt/models/cache:/root/.cache -e HF_HOME=/mnt/models/hf ghcr.io/ggml-org/llama.cpp@sha256:b51c252687249a6990ae1c168156cb215c954bfd71f9c434eda9a065b1a40dbe -m /mnt/models/gguf/Qwen3.8-Flash-Next-IQ3/UD-IQ3_XXS/Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 1 --jinja -t 16 -tb 28 --metrics -b 4096 -ub 2048 -lm none --spec-type draft-mtp -md /mnt/models/gguf/Qwen3.8-Flash-Next/mtp-Qwen3.8-Flash-Next-Q8_0.gguf --spec-draft-n-max 1

Extra flags

--d --name lsv-fn-iq3xxs-mtp1-np1 --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 81 GB model does not fit the 48 GB GPU, so llama.cpp's --fit kept the layers on the GPU and placed part of the MoE expert weights in system RAM on the CPU (loaded with -lm none, so the CPU part is in ordinary RAM, not memory-mapped); the exact split is not logged at the default verbosity; 47522 MiB of VRAM in use after load, 16 generation threads, 28 batch threads, flash attention on, 1 slot, context 32768. MTP speculative decoding from mtp-Qwen3.8-Flash-Next-Q8_0.gguf, up to 1 draft tokens. GGUF unsloth/Qwen3.8-Flash-Next-GGUF @ 766911a6b7369840a91dbcd95f9f997acaab6cd6, file Qwen3.8-Flash-Next-UD-IQ3_XXS-00001-of-00003.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 42.5, 39.3, 40.3 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, 11:40 PM

Last edited

—

Qwen3.8-27B-INT4

27B · Qwen

RTX A6000 48GB
vllmINT4_W4A1653362.31,2155,519 ms0k
Show all details for Qwen3.8-27B-INT4

Model

RedHatAI/Qwen3.8-27B-INT4

Display name

Qwen3.8-27B-INT4

Base model

Qwen3.8-27B

Revision

91bd022d5b49442a868bc35008f6c21e1860edfa

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

533 tok/s

Prefill

62.3 tok/s

Total

1,215 tok/s

TTFT

5,519 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

48

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

INT4_W4A16

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

48

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-v27-int4 --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/Qwen3.8-27B-INT4 --served-model-name Qwen3.8-27B-INT4 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-v27-int4 --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. 48 requests sent at once (batch 48, lmx --concurrency 48); 533.4 tok/s is the aggregate decode rate across all 48 streams, about 11.1 tok/s per stream, and the 5519 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 (346 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 48: 533.6, 533.4, 533.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-int4, 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, 11:30 PM

Last edited

—

Qwen3.8-27B-INT4

27B · Qwen

RTX A6000 48GB
vllmINT4_W4A16106574236607 ms0k
Show all details for Qwen3.8-27B-INT4

Model

RedHatAI/Qwen3.8-27B-INT4

Display name

Qwen3.8-27B-INT4

Base model

Qwen3.8-27B

Revision

91bd022d5b49442a868bc35008f6c21e1860edfa

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

106 tok/s

Prefill

574 tok/s

Total

236 tok/s

TTFT

607 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

3

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

INT4_W4A16

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

3

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-v27-int4 --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/Qwen3.8-27B-INT4 --served-model-name Qwen3.8-27B-INT4 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-v27-int4 --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. 3 requests sent at once (batch 3, lmx --concurrency 3); 105.8 tok/s is the aggregate decode rate across all 3 streams, about 35.3 tok/s per stream, and the 607 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 (347 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 3: 105.9, 105.8, 105.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-int4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

—

Submitted

Oct 27, 2026, 11:30 PM

Last edited

—

Qwen3.8-27B-INT4

27B · Qwen

RTX A6000 48GB
vllmINT4_W4A1644296.69853,603 ms0k
Show all details for Qwen3.8-27B-INT4

Model

RedHatAI/Qwen3.8-27B-INT4

Display name

Qwen3.8-27B-INT4

Base model

Qwen3.8-27B

Revision

91bd022d5b49442a868bc35008f6c21e1860edfa

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

442 tok/s

Prefill

96.6 tok/s

Total

985 tok/s

TTFT

3,603 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

24

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

INT4_W4A16

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

24

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-v27-int4 --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/Qwen3.8-27B-INT4 --served-model-name Qwen3.8-27B-INT4 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-v27-int4 --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. 24 requests sent at once (batch 24, lmx --concurrency 24); 441.5 tok/s is the aggregate decode rate across all 24 streams, about 18.4 tok/s per stream, and the 3603 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 (346 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 24: 441.5, 441.5, 442 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-int4, kasa-testing campaign ai/vm/cuda-vm/2026-10-26-a6000-localmaxxing.

Reactions

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Submitted

Oct 27, 2026, 11:30 PM

Last edited

—

Qwen3.8-27B-INT4

27B · Qwen

RTX A6000 48GB
vllmINT4_W4A163171836941,931 ms0k
Show all details for Qwen3.8-27B-INT4

Model

RedHatAI/Qwen3.8-27B-INT4

Display name

Qwen3.8-27B-INT4

Base model

Qwen3.8-27B

Revision

91bd022d5b49442a868bc35008f6c21e1860edfa

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

317 tok/s

Prefill

183 tok/s

Total

694 tok/s

TTFT

1,931 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

348

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

12

Hardware class

DISCRETE_GPU

Hardware

RTX A6000 48GB

GPU slots

—

GPU count

1

VRAM

48GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Debian GNU/Linux 13 (trixie)

Power

—

Engine

vllm

Engine version

0.32.0

Quantization

INT4_W4A16

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

12

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

—

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-v27-int4 --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/Qwen3.8-27B-INT4 --served-model-name Qwen3.8-27B-INT4 --host 0.0.0.0 --port 8000 --gpu-memory-utilization 0.92 --max-model-len 32768 --limit-mm-per-prompt '{"image":0,"video":0}'

Extra flags

--d --name lsv-v27-int4 --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. 12 requests sent at once (batch 12, lmx --concurrency 12); 316.7 tok/s is the aggregate decode rate across all 12 streams, about 26.4 tok/s per stream, and the 1931 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 (348 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 12: 316.9, 316.7, 317.1 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-int4, 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, 11:30 PM

Last edited

—

Qwen3.8-27B-FP8

27B · Qwen

RTX A6000 48GB
vllmFP843752.29956,621 ms0k
Show all details for Qwen3.8-27B-FP8

Model

Qwen/Qwen3.8-27B-FP8

Display name

Qwen3.8-27B-FP8

Base model

Qwen3.8-27B

Revision

017b9c7af6b5689d5dd426a76e0bc077eb5ca20a

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

437 tok/s

Prefill

52.2 tok/s

Total

995 tok/s

TTFT

6,621 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

48

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

48

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-v27-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/Qwen3.8-27B-FP8 --served-model-name Qwen3.8-27B-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-v27-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. 48 requests sent at once (batch 48, lmx --concurrency 48); 437.1 tok/s is the aggregate decode rate across all 48 streams, about 9.1 tok/s per stream, and the 6621 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 (346 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 48: 437.1, 436.5, 437.3 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-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, 11:30 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppUD-Q5_K_M70.937.81279,168 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

70.9 tok/s

Prefill

37.8 tok/s

Total

127 tok/s

TTFT

9,168 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q5_K_M

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q5-k-m-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q5_K_M.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q5-k-m-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 70.9 tok/s is the aggregate decode rate across all 8 streams, about 8.9 tok/s per stream, and the 9168 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q5_K_M.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: 72.3, 70.9, 72.2 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 11:26 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppUD-Q5_K_M33.914268.52,427 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

33.9 tok/s

Prefill

142 tok/s

Total

68.5 tok/s

TTFT

2,427 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

345

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

2

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q5_K_M

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

Continuous batching

—

CPU offload

—

CPU layers

—

Rope scaling

—

Rope scale

—

Yarn ext factor

—

Engine quant

—

SGLang quant

—

GPU mem util

—

Max running seqs

—

Scheduler delay

—

Num parallel

8

Concurrency

2

Spec decoding

no

Spec method

—

Spec model

—

Spec draft model

—

Spec tokens

—

Spec ngram

—

Spec draft TP

—

Spec draft window

—

MTP enabled

no

MTP draft layers

—

Temperature

16

Top P

—

Top K

—

Min P

—

Repeat penalty

—

Mirostat

—

Command

docker run -d --name lsv-q27-q5-k-m-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q5_K_M.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q5-k-m-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 2 requests sent at once (lmx --concurrency 2); 33.9 tok/s is the aggregate decode rate across all 2 streams, about 16.9 tok/s per stream, and the 2427 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q5_K_M.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: 34, 33.8, 33.9 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

—

Submitted

Oct 27, 2026, 11:26 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppUD-Q5_K_M17.827938.81,244 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

17.8 tok/s

Prefill

279 tok/s

Total

38.8 tok/s

TTFT

1,244 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

347

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

1

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q5_K_M

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q5-k-m-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q5_K_M.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q5-k-m-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q5_K_M.gguf. Canonical reasoning-v1, lmx single-shot requests after one discarded warm-up. Measured requests: 17.8, 17.9, 17.8 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, 11:26 PM

Last edited

—

Qwen3.8-27B

27B · Qwen

Intel Arc Pro B65 32GB
llama.cppUD-Q4_K_XL54.538.51049,005 ms0k
Show all details for Qwen3.8-27B

Model

Qwen/Qwen3.8-27B

Display name

Qwen3.8-27B

Base model

—

Revision

4ca720788d1e01f1bff70c033e0d0028fd02e502

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

54.5 tok/s

Prefill

38.5 tok/s

Total

104 tok/s

TTFT

9,005 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

348

Output tokens

256

Prefill tokens

0

Context length

2048

Batch size

8

Hardware class

DISCRETE_GPU

Hardware

Intel Arc Pro B65 32GB

GPU slots

—

GPU count

1

VRAM

32GB

Chip vendor

—

Chip family

—

Chip variant

—

Unified memory

—

NPU TOPS

—

CPU

AMD EPYC 7343 16-Core Processor

RAM

126GB

OS

Ubuntu 26.04.1 LTS

Power

—

Engine

llama.cpp

Engine version

b11913

Quantization

UD-Q4_K_XL

Backend

xpu

Tensor parallel

—

Pipeline parallel

—

GPU layers

99

Split mode

—

KV cache dtype

—

KV cache size

—

Prefix caching

—

Attention backend

—

Flash attention

yes

Chunked prefill

—

Prefill chunk

—

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-q27-q4-k-xl-np8 --device /dev/dri:/dev/dri --shm-size 32g -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:bf9a9d6290f917eb040e4313ecdab2a88757d18fc16311298a88b6c0e4a8c7fb -m /mnt/models/gguf/Qwen3.8-27B/Qwen3.8-27B-UD-Q4_K_XL.gguf --host 0.0.0.0 --port 8000 -fa on -c 32768 -np 8 --jinja -t 16 -tb 28 --metrics -ngl 99

Extra flags

--d --name lsv-q27-q4-k-xl-np8 --shm-size 32g --p 127.0.0.1:8000:8000 --e HF_HOME=/mnt/models/hf --jinja --tb 28 --metrics

Notes

CONCURRENCY TEST, NOT A SINGLE-USER RESULT. 8 requests sent at once (lmx --concurrency 8); 54.5 tok/s is the aggregate decode rate across all 8 streams, about 6.8 tok/s per stream, and the 9005 ms time to first token is under that load. KASA b65-vm: 1 x Intel Arc Pro B65 32 GB (PCIe passthrough, xe driver, Ubuntu 26.04 kernel 7.0) in a VM with 28 vCPU of an AMD EPYC 7343 and 128 GiB. llama.cpp llama-server b11913 (image ghcr.io/ggml-org/llama.cpp), all layers on the GPU (-ngl 99), 16 generation threads, 28 batch threads, flash attention on, 8 server slots of 4096 tokens each, context 32768. GGUF unsloth/Qwen3.8-27B-GGUF @ 4ca720788d1e01f1bff70c033e0d0028fd02e502, file Qwen3.8-27B-UD-Q4_K_XL.gguf. Each request: LocalMaxxing's canonical reasoning-v1 prompt with a unique nonce, 256 output tokens. One discarded warm-up batch, then 3 measured batches: 54.6, 54.6, 54.5 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Selection rule applied to every run in this round: the median of the 2 measured requests whose lmx promptTokens is a whole number; the others report a fractional median, which LocalMaxxing refuses.

Reactions

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Submitted

Oct 27, 2026, 11:26 PM

Last edited

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Qwen3.8-27B-FP8

27B · Qwen

RTX A6000 48GB
vllmFP832479.77274,335 ms0k
Show all details for Qwen3.8-27B-FP8

Model

Qwen/Qwen3.8-27B-FP8

Display name

Qwen3.8-27B-FP8

Base model

Qwen3.8-27B

Revision

017b9c7af6b5689d5dd426a76e0bc077eb5ca20a

Family

Qwen

Parameters

27B

Active params

—

MoE

no

Output

324 tok/s

Prefill

79.7 tok/s

Total

727 tok/s

TTFT

4,335 ms

Peak VRAM

—

Power draw

—

Hardware cost

—

Prompt tokens

346

Output tokens

256

Prefill tokens

0

Context length

32768

Batch size

24

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

24

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-v27-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/Qwen3.8-27B-FP8 --served-model-name Qwen3.8-27B-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-v27-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. 24 requests sent at once (batch 24, lmx --concurrency 24); 323.8 tok/s is the aggregate decode rate across all 24 streams, about 13.5 tok/s per stream, and the 4335 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 (346 tokens) with a unique nonce, 256 output tokens, temperature 0. One discarded warm-up batch, then 3 measured batches of 24: 324.2, 323.8, 323.8 tok/s; this is the median. Not eligible for the Verified badge, which requires batch 1. Config v27-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, 11:20 PM

Last edited

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