Can I Run Mistral Small 3.1 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?

Superseded model. Mistral Small 3.1 has been superseded by Mistral Small 3.2. This page is kept for reference; the newer family is a better starting point. View Mistral Small 3.2 →

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 17 marca 2025

Yes

Yes — Mistral Small 3.1 24B at Q2_K needs about 9.9 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~2.1 GB spare), at ~29.9 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q2_K · Estimated speed: ~29.9 tok/s

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board power Deploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

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

Mistral Small 3.1 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	49.3 GB	✗ No	—	—	47.2 GB
Q8_0	27.2 GB	✗ No	—	—	25.1 GB
Q6_K	21.5 GB	✗ No	—	—	19.4 GB
Q5_K_M	18.9 GB	✗ No	—	—	16.7 GB
Q4_K_M	16.4 GB	✗ No	—	—	14.2 GB
Q3_K_M	12.2 GB	✗ No	—	—	10.1 GB
Q2_K	9.9 GB	✓ Yes	16K	~29.9 tok/s	7.8 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 Mistral Small 3.1 does not fit and would need CPU offload or different hardware.

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 computed from this model's published attention configuration.

FAQ