Qwen2.5-Coder-14B-Instruct on RTX 4090 (24GB) + AMD EPYC 7642 48-Core Processor (48c) + 252GB
MeasuredOn 2026-10-14, Qwen2.5-Coder-14B-Instruct Q4_K_M ran at 89.9 tok/s (chat-short) on an RTX 4090 (24GB) with llama.cpp. Run r_eqnsqmgz29a.
RTX 4090 (24GB) + AMD EPYC 7642 48-Core Processor (48c) + 252GB
suite suite-v1
cli 0.0.7
signedq3dA19efmQ…
Embed badgesubmitted Oct 14, 2026Workload results
| Workload | Backend | Model | decode tok/s | prefill tok/s | TTFT | p50 | p95 |
|---|---|---|---|---|---|---|---|
| chat-short | llama.cpp@0.6.0-dev (build 1, commit df18bee) | Qwen2.5-Coder-14B-InstructQ4_K_M | 89.87tok/s | 2,292.6tok/s | 57.1ms | 11.0ms | 11.2ms |
| chat-long | llama.cpp@0.6.0-dev (build 1, commit df18bee) | Qwen2.5-Coder-14B-InstructQ4_K_M | 84.17tok/s | 4,989.1tok/s | 635ms | 11.8ms | 12.0ms |
| concurrent-decode | llama.cpp@0.6.0-dev (build 1, commit df18bee) | Qwen2.5-Coder-14B-InstructQ4_K_M | 88.90tok/s | — | — | 11.2ms | 11.3ms |
| agent-trace | llama.cpp@0.6.0-dev (build 1, commit df18bee) | Qwen2.5-Coder-14B-InstructQ4_K_M | 86.64tok/s | 14,684.0tok/s | 142ms | 11.4ms | 11.8ms |
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_eqnsqmgz29aRelated benchmarks
- More Qwen2.5-Coder-14B-Instruct benchmarks — every backend and rig that has run this model.
- More RTX 4090 (24GB) LLM benchmarks — every model measured on this hardware.
Provenance
- Run ID
- r_eqnsqmgz29a
- Fingerprint hash
- Public key
- q3dA19efmQai5SMVtPEF4zPDSTtCGRIPbP1h8bUH9pM=
- Received
- 2026-10-14 09:22:46