Qwen3-32B on RTX 3090 (24GB) + AMD EPYC 7282 16-Core Processor (16c) + 126GB
MeasuredOn 2026-10-17, Qwen3-32B Q4_K_M ran at 38.5 tok/s (chat-short) on an RTX 3090 (24GB) with llama.cpp. Run r_km62sybxga6.
RTX 3090 (24GB) + AMD EPYC 7282 16-Core Processor (16c) + 126GB
suite suite-v1
cli 0.0.7
signed8NwfMSefBi…
Embed badgesubmitted Oct 17, 2026Workload results
| Workload | Backend | Model | decode tok/s | prefill tok/s | TTFT | p50 | p95 |
|---|---|---|---|---|---|---|---|
| chat-short | llama.cpp@0.7.0-dev (build 1, commit ea0c4c2) | Qwen3-32BQ4_K_M | 38.45tok/s | 589.5tok/s | 187ms | 26.0ms | 26.1ms |
| chat-long | llama.cpp@0.7.0-dev (build 1, commit ea0c4c2) | Qwen3-32BQ4_K_M | 36.94tok/s | 1,218.2tok/s | 2,583ms | 27.0ms | 27.2ms |
| concurrent-decode | llama.cpp@0.7.0-dev (build 1, commit ea0c4c2) | Qwen3-32BQ4_K_M | 38.05tok/s | — | — | 26.3ms | 26.3ms |
| agent-trace | llama.cpp@0.7.0-dev (build 1, commit ea0c4c2) | Qwen3-32BQ4_K_M | 37.57tok/s | 3,287.7tok/s | 624ms | 26.5ms | 27.0ms |
Reproduce on your machine
Start with this model and workload selection. This command does not capture the original artifact, backend version or server settings:
Match quantization, context capacity, caching and runtime settings for a useful comparison. Duration varies by workload and hardware; the published client may differ from this run's version. How it's measured.
Share this run
Copy the short link, a Reddit-ready summary, or a README badge. Every number is the measured result on this page, and each badge links back to it.
https://llm-speed.com/r/r_km62sybxga6Related benchmarks
- More Qwen3-32B benchmarks — every backend and rig that has run this model.
- More RTX 3090 (24GB) LLM benchmarks — every model measured on this hardware.
Provenance
- Run ID
- r_km62sybxga6
- Fingerprint hash
- Public key
- 8NwfMSefBis+pXXbzlBJlA9tXf48zRzdwbuR65a8OsY=
- Received
- 2026-10-17 02:10:39