Written by Jakub Rusinowski · Last updated November 12, 2024
Yes, but it is tight
Yes, but it is tight — Qwen 2.5 Coder 32B at Q4_K_M needs about 22.3 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~1.7 GB before the runtime starts swapping. Expect ~34.2 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: medium · Recommended quantization: Q4_K_M · Estimated speed: ~34.2 tok/s
| Usable memory for models | 24 GB |
| Memory bandwidth | 1008 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 24 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 66.9 GB | ✗ No | — | — | 64 GB |
| Q8_0 | 36.9 GB | ✗ No | — | — | 34 GB |
| Q6_K | 29.2 GB | ✗ No | — | — | 26.2 GB |
| Q5_K_M | 25.6 GB | ✗ No | — | — | 22.7 GB |
| Q4_K_M | 22.3 GB | ✓ Yes | 8K | ~34.2 tok/s | 19.3 GB |
| Q3_K_M | 16.6 GB | ✓ Yes | 32K | ~46.1 tok/s | 13.6 GB |
| Q2_K | 13.5 GB | ✓ Yes | 32K | ~56.9 tok/s | 10.5 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen 2.5 72B Instruct | 47 GB | ✗ Too large | — |
| Qwen 2.5 Coder 32B | 22.3 GB | ✓ Fits | ~34.2 tok/s |
| Qwen 2.5 14B Instruct | 10.9 GB | ✓ Fits | ~69 tok/s |
| Qwen 2.5 7B Instruct | 5.9 GB | ✓ Fits | ~115.9 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, but it is tight — Qwen 2.5 Coder 32B at Q4_K_M needs about 22.3 GB of the 24 GB usable on RTX 4090 Desktop (24 GB VRAM, 64 GB RAM), leaving only ~1.7 GB before the runtime starts swapping. Expect ~34.2 tok/s (estimated), with room for about 8,192 tokens of context.
Q4_K_M — it needs about 22.3 GB of the 24 GB available, downloads as roughly 19.3 GB, and runs at an estimated 34.2 tokens/sec with up to 8K of context.
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
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