Can I Run Devstral on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
作者： Jakub Rusinowski · 最后更新： 2026年9月11日
Yes
Yes — Devstral Small 2505 24B at Q8_0 needs about 27.6 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~4.4 GB spare), at ~46 tok/s (estimated), with room for about 32,768 tokens of context.
Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~46 tok/s
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RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) — what it gives a model
| Usable memory for models | 32 GB |
| Memory bandwidth | 1792 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Devstral on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM): memory by quantization
| Quant | Memory needed | Fits 32 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 50.1 GB | ✗ No | — | — | 48 GB |
| Q8_0 | 27.6 GB | ✓ Yes | 32K | ~46 tok/s | 25.5 GB |
| Q6_K | 21.8 GB | ✓ Yes | 64K | ~57.5 tok/s | 19.7 GB |
| Q5_K_M | 19.2 GB | ✓ Yes | 64K | ~64.9 tok/s | 17 GB |
| Q4_K_M | 16.6 GB | ✓ Yes | 64K | ~73.9 tok/s | 14.5 GB |
| Q3_K_M | 12.4 GB | ✓ Yes | 64K | ~96.6 tok/s | 10.2 GB |
| Q2_K | 10 GB | ✓ Yes | 128K | ~116.1 tok/s | 7.9 GB |
Which Devstral sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|---|---|---|
| Devstral-2 123B | 77.9 GB | ✗ Too large | — |
| Devstral Small 2505 24B | 16.6 GB | ✓ Fits | ~73.9 tok/s |
| Devstral Small 2 24B | 16.6 GB | ✓ Fits | ~73.9 tok/s |
What to watch out for
- 1 larger variant of Devstral does not fit and would need CPU offload or different hardware.
RTX 5090 desktop limitations
- 575 W board power — budget for a 1000 W+ PSU and the heat it puts into the room.
- Models larger than 32 GB must offload to system RAM, which costs roughly an order of magnitude in speed.
Recommended setup
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
How these numbers are calculated
- Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
- 32 GB of VRAM on the NVIDIA GeForce RTX 5090 at 1792 GB/s.
- 64 GB of system RAM available for CPU offload when a model exceeds VRAM.
- Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
- KV cache is computed from this model's published attention configuration.
FAQ
Can I run Devstral on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Yes — Devstral Small 2505 24B at Q8_0 needs about 27.6 GB of the 32 GB usable on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM) (~4.4 GB spare), at ~46 tok/s (estimated), with room for about 32,768 tokens of context.
Which quantization of Devstral should I use on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Q8_0 — it needs about 27.6 GB of the 32 GB available, downloads as roughly 25.5 GB, and runs at an estimated 46 tokens/sec with up to 32K of context.
What limits Devstral on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)?
Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.
Which runtime should I use?
Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving
Other Computers
Other Models on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- EXAONE 3.5 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Falcon 3 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Gemma 2 Family on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Gemma 3 on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
- Gemma 3n on RTX 5090 Desktop (32 GB VRAM, 64 GB RAM)
Devstral on GPUs
- Devstral on NVIDIA GeForce RTX 5090
- Devstral on NVIDIA GeForce RTX 5080
- Devstral on NVIDIA GeForce RTX 5070 Ti
- Devstral on NVIDIA GeForce RTX 5070