Can I Run Devstral on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Autor: Jakub Rusinowski · Ostatnia aktualizacja: 11 września 2026
Yes
Yes — Devstral Small 2505 24B at Q3_K_M needs about 12.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~3.6 GB spare), at ~57.5 tok/s (estimated), with room for about 16,384 tokens of context.
Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~57.5 tok/s
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RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 16 GB |
| Memory bandwidth | 960 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Devstral on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 16 GB? | Max context | Est. speed | Download |
|---|---|---|---|---|---|
| F16 | 50.1 GB | ✗ No | — | — | 48 GB |
| Q8_0 | 27.6 GB | ✗ No | — | — | 25.5 GB |
| Q6_K | 21.8 GB | ✗ No | — | — | 19.7 GB |
| Q5_K_M | 19.2 GB | ✗ No | — | — | 17 GB |
| Q4_K_M | 16.6 GB | ✗ No | — | — | 14.5 GB |
| Q3_K_M | 12.4 GB | ✓ Yes | 16K | ~57.5 tok/s | 10.2 GB |
| Q2_K | 10 GB | ✓ Yes | 32K | ~70.7 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 | ✗ Too large | — |
| Devstral Small 2 24B | 16.6 GB | ✗ Too large | — |
What to watch out for
- Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
- 3 larger variants of Devstral do not fit and would need CPU offload or different hardware.
RTX 5080 desktop limitations
- 16 GB VRAM is the binding constraint, not compute — a slower 24 GB card runs strictly more models.
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.
- 16 GB of VRAM on the NVIDIA GeForce RTX 5080 at 960 GB/s.
- 32 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 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Yes — Devstral Small 2505 24B at Q3_K_M needs about 12.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~3.6 GB spare), at ~57.5 tok/s (estimated), with room for about 16,384 tokens of context.
Which quantization of Devstral should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?
Q3_K_M — it needs about 12.4 GB of the 16 GB available, downloads as roughly 10.2 GB, and runs at an estimated 57.5 tokens/sec with up to 16K of context.
What limits Devstral on RTX 5080 Desktop (16 GB VRAM, 32 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 5080 Desktop (16 GB VRAM, 32 GB RAM)
- EXAONE 3.5 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Falcon 3 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Gemma 2 Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Gemma 3 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
- Gemma 3n on RTX 5080 Desktop (16 GB VRAM, 32 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