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Speed test matrix

Qwen3-8B-FP8 on RTX 3090 24GB

vllm · 0.32.0 · FP83 contexts (0–32K) × 8 concurrency levels (1–128) · prefill at 8K, 16K, 32K
by @Sandbergllm-inference-bench v0.8.0Measured

Hardware

Type
Discrete GPU
GPU
RTX 3090
VRAM
24 GB
CPU
Intel Core i7-9700K (8C)
RAM
32 GB
OS
Ubuntu 24.04.5

Engine

Engine
vllm
Version
0.32.0
Quantization
FP8
Backend
cuda
Build
vllm/vllm-openai:v0.32.0-x86_64

Run

Served model
bench
Revision
main
Decode test
Fixed duration
Prefill mode
Measured during decode warm-up
Time per measurement
30 s
Max output
8,192 tokens

Launch command

# Untuned out-of-the-box baseline. Spec decode: none
# 1x RTX 3090 24GB (Zagitta/gpu-unlock), 420 W limit, PCIe Gen3 x16 | Intel Core i7-9700K 8C | 32 GB DDR4 | Ubuntu 24.04.5, NVIDIA driver 580.178.04
vllm serve Qwen/Qwen3-8B-FP8 --max-model-len 40960 --gpu-memory-utilization 0.94 --max-num-seqs 128 --enable-prefix-caching --trust-remote-code

Prefill

prompt tok/s by context length
8K context5,7311,440 ms TTFT
16K context5,0903,212 ms TTFT
32K context4,0777,983 ms TTFT

Sustained decode

total tok/s · rows are context length, columns are simultaneous requests
Context1248163264128
086.631 ms TTFT167.027 ms TTFT318.840 ms TTFT580.652 ms TTFTOver capacityOver capacityOver capacityOver capacity
16K70.069 ms TTFTLoopedOver capacityOver capacityOver capacityOver capacityOver capacityOver capacity
32K58.9123 ms TTFT91.8169 ms TTFTOver capacityOver capacityOver capacityOver capacityOver capacityOver capacity

Looped: the model kept repeating the same text, so the benchmark discarded this measurement. Over capacity: not run because this many requests at this context would exceed the server's KV cache.

TTFT is measured after the benchmark warms the server's prompt cache, so it mostly excludes prefill.

All measurements

Latencies are medians. Total tok/s is output across all simultaneous requests.
PhaseContextRequests at onceStatusTotal tok/sPer request tok/sTTFTTime between tokensAverage active requestsCompleted requestsOutput tokensMeasured for
Prefill8K1Valid5,731.0—1,440 ms——1——
Prefill16K1Valid5,090.0—3,212 ms——1——
Prefill32K1Valid4,077.0—7,983 ms——1——
Sustained decode01Valid86.687.031 ms11.5 ms1.002,59930.0 s
Sustained decode02Valid167.084.227 ms11.9 ms2.005,00830.0 s
Sustained decode04Valid318.880.840 ms12.4 ms4.009,56430.0 s
Sustained decode08Valid580.674.352 ms13.5 ms8.0017,41630.0 s
Sustained decode016Over capacity————————
Sustained decode032Over capacity————————
Sustained decode064Over capacity————————
Sustained decode0128Over capacity————————
Sustained decode16K1Valid70.070.169 ms14.3 ms1.002,10130.0 s
Sustained decode16K2Looped—————00—
Sustained decode16K4Over capacity————————
Sustained decode16K8Over capacity————————
Sustained decode16K16Over capacity————————
Sustained decode16K32Over capacity————————
Sustained decode16K64Over capacity————————
Sustained decode16K128Over capacity————————
Sustained decode32K1Valid58.959.0123 ms17.0 ms1.001,76630.0 s
Sustained decode32K2Valid91.846.0169 ms21.8 ms2.002,75330.0 s
Sustained decode32K4Over capacity————————
Sustained decode32K8Over capacity————————
Sustained decode32K16Over capacity————————
Sustained decode32K32Over capacity————————
Sustained decode32K64Over capacity————————
Sustained decode32K128Over capacity————————

Looped: the model kept repeating the same text, so the benchmark discarded this measurement. Over capacity: not run because this many requests at this context would exceed the server's KV cache.