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Speed test matrix

Qwen3-1.7B-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
AMD Ryzen 9 7900X (12C)
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), 350 W limit (VBIOS max), PCIe Gen4 x16 | AMD Ryzen 9 7900X 12C | 32 GB DDR5 | Ubuntu 24.04.5, NVIDIA driver 595.99.02
vllm serve Qwen/Qwen3-1.7B-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 context20.1K411 ms TTFT
16K context16.9K969 ms TTFT
32K context12.2K2,658 ms TTFT

Sustained decode

total tok/s · rows are context length, columns are simultaneous requests
Context1248163264128
0Looped394.315 ms TTFT672.517 ms TTFT1,068.024 ms TTFT1,630.651 ms TTFTOver capacityOver capacityOver capacity
16K155.143 ms TTFT233.757 ms TTFT367.486 ms TTFTOver capacityOver capacityOver capacityOver capacityOver capacity
32K117.878 ms TTFT162.4106 ms TTFT237.0147 ms TTFTOver 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
Prefill8K1Valid20.1K—411 ms——1——
Prefill16K1Valid16.9K—969 ms——1——
Prefill32K1Valid12.2K—2,658 ms——1——
Sustained decode01Looped—————00—
Sustained decode02Valid394.3205.115 ms4.9 ms2.0011,82830.0 s
Sustained decode04Valid672.5178.617 ms5.6 ms4.0020,17230.0 s
Sustained decode08Valid1,068.0146.124 ms6.8 ms8.0032,04130.0 s
Sustained decode016Valid1,630.6114.751 ms8.7 ms16.0048,91230.0 s
Sustained decode032Over capacity————————
Sustained decode064Over capacity————————
Sustained decode0128Over capacity————————
Sustained decode16K1Valid155.1156.143 ms6.4 ms1.004,65430.0 s
Sustained decode16K2Valid233.7117.957 ms8.5 ms2.007,01030.0 s
Sustained decode16K4Valid367.492.986 ms10.8 ms4.0011,02030.0 s
Sustained decode16K8Over capacity————————
Sustained decode16K16Over capacity————————
Sustained decode16K32Over capacity————————
Sustained decode16K64Over capacity————————
Sustained decode16K128Over capacity————————
Sustained decode32K1Valid117.8118.278 ms8.5 ms1.003,53430.0 s
Sustained decode32K2Valid162.481.4106 ms12.3 ms2.004,87030.0 s
Sustained decode32K4Valid237.059.4147 ms16.8 ms4.007,10830.0 s
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.