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Qwen2.5 14B Instruct on the NVIDIA RTX 4090 Laptop
Qwen2.5 14B Instruct runs at about 18.8 tokens/s on the NVIDIA RTX 4090 Laptop (measured, Q4_K_M, llamafile, LocalScore).
Q4_K_M · llamafile
same run
For comparison, our size-scaling estimate for this combination is 27.0 tok/s.
Will it fit? Q4_K_M fits
| GGUF file, Q4_K_M | 9.0 GB | Hugging Face file size |
| Runtime + KV-cache allowance | 1.2 GB | estimate about an 8K-token context |
| Memory needed | 10.2 GB | |
| Usable on the NVIDIA RTX 4090 Laptop | 15.5 GB | 16 GB minus 0.5 GB for the driver |
Check another quant or memory size in the Will it run calculator
Every run
| Quant | Backend | Generate tok/s | Prompt tok/s | Test | Build / by / notes | Source |
|---|---|---|---|---|---|---|
| Q4_K_M | llamafile | 18.8 | 1,039 | LocalScore avg | LocalScore rank 70/420 | LocalScore (localscore.ai) |
tg128 = generating 128 tokens; pp512 = reading a 512-token prompt (llama-bench). LocalScore averages several prompt and output lengths, so its numbers run lower than tg128. "decode, llama-server" = single-stream generation speed measured against a running llama-server (no speculative decoding).
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