Can I Run Devstral on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?
Written by Jakub Rusinowski · Last updated August 15, 2026
Yes, but it is tight
Yes, but it is tight — Devstral-2 22B at Q3_K_M needs about 11.8 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM), leaving only ~0.2 GB before the runtime starts swapping. Expect ~25 tok/s (estimated), with room for about 8,192 tokens of context.
Confidence: low · Recommended quantization: Q3_K_M · Estimated speed: ~25 tok/s
RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) — what it gives a model
| Usable memory for models | 12 GB |
| Memory bandwidth | 360 GB/s |
| Form factor | Desktop |
| Operating system | Windows or Linux |
| Memory upgradeable | Yes |
Devstral on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM): memory by quantization
| Quant | Memory needed | Fits 12 GB? | Max context | Est. speed | Download |
|---|
| F16 | 46.4 GB | ✗ No | — | — | 44 GB |
| Q8_0 | 25.8 GB | ✗ No | — | — | 23.4 GB |
| Q6_K | 20.5 GB | ✗ No | — | — | 18 GB |
| Q5_K_M | 18 GB | ✗ No | — | — | 15.6 GB |
| Q4_K_M | 15.7 GB | ✗ No | — | — | 13.3 GB |
| Q3_K_M | 11.8 GB | ✓ Yes | 8K | ~25 tok/s | 9.4 GB |
| Q2_K | 9.6 GB | ✓ Yes | 16K | ~31.2 tok/s | 7.2 GB |
Which Devstral sizes fit
| Variant | Needs at Q4_K_M | Fits? | Est. speed |
|---|
| Devstral-2 123B | 77.9 GB | ✗ Too large | — |
| Devstral Small 24B | 16.6 GB | ✗ Too large | — |
| Devstral-2 22B | 15.7 GB | ✗ Too large | — |
What to watch out for
- Only ~0.2 GB of headroom at Q3_K_M: a longer context or a second application can push this into swapping.
- 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.
- This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
RTX 3060 12 GB desktop limitations
- The budget entry point to local AI: 12 GB runs 7–14B models well and nothing larger without offload.
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.
- 12 GB of VRAM on the NVIDIA GeForce RTX 3060 (12GB) at 360 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 inferred from the parameter count because this model does not publish its full attention configuration.
FAQ
Can I run Devstral on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?
Yes, but it is tight — Devstral-2 22B at Q3_K_M needs about 11.8 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM), leaving only ~0.2 GB before the runtime starts swapping. Expect ~25 tok/s (estimated), with room for about 8,192 tokens of context.
Which quantization of Devstral should I use on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?
Q3_K_M — it needs about 11.8 GB of the 12 GB available, downloads as roughly 9.4 GB, and runs at an estimated 25 tokens/sec with up to 8K of context.
What limits Devstral on RTX 3060 12 GB Desktop (12 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 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)
Devstral on GPUs
What This Model Is Good At
Model & Tools
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