Written by Jakub Rusinowski · Last updated January 25, 2025
Yes — comfortably
Yes, comfortably — Qwen 2.5 VL 7B Instruct at Q8_0 needs about 10.1 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~21.9 GB spare and running at ~111.4 tok/s (estimated), with room for about 65,536 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~111.4 tok/s
| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 17.8 GB | ✓ Yes | 64K | ~67.8 tok/s | 16.6 GB |
| Q8_0 | 10.1 GB | ✓ Yes | 64K | ~111.4 tok/s | 8.8 GB |
| Q6_K | 8.1 GB | ✓ Yes | 64K | ~133.7 tok/s | 6.8 GB |
| Q5_K_M | 7.1 GB | ✓ Yes | 64K | ~147.2 tok/s | 5.9 GB |
| Q4_K_M | 6.3 GB | ✓ Yes | 64K | ~162.7 tok/s | 5 GB |
| Q3_K_M | 4.8 GB | ✓ Yes | 64K | ~198 tok/s | 3.5 GB |
| Q2_K | 4 GB | ✓ Yes | 64K | ~224.8 tok/s | 2.7 GB |
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Qwen 2.5 VL 72B Instruct | 47.8 GB | ✗ Too large | — |
| Qwen 2.5 VL 7B Instruct | 6.3 GB | ✓ Fits | ~162.7 tok/s |
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Yes, comfortably — Qwen 2.5 VL 7B Instruct at Q8_0 needs about 10.1 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM), leaving ~21.9 GB spare and running at ~111.4 tok/s (estimated), with room for about 65,536 tokens of context.
Q8_0 — it needs about 10.1 GB of the 32 GB available, downloads as roughly 8.8 GB, and runs at an estimated 111.4 tokens/sec with up to 64K 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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