Can I Run Magistral Small on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes

Yes — Magistral Small 24B at Q2_K needs about 10.4 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.6 GB spare), at ~28.9 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q2_K · Estimated speed: ~28.9 tok/s

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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

Magistral Small 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	50.5 GB	✗ No	—	—	48 GB
Q8_0	28 GB	✗ No	—	—	25.5 GB
Q6_K	22.2 GB	✗ No	—	—	19.7 GB
Q5_K_M	19.5 GB	✗ No	—	—	17 GB
Q4_K_M	17 GB	✗ No	—	—	14.5 GB
Q3_K_M	12.7 GB	✗ No	—	—	10.2 GB
Q2_K	10.4 GB	✓ Yes	8K	~28.9 tok/s	7.9 GB

What to watch out for

Q2_K 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.
1 larger variant of Magistral Small does 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.

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