Can I Run Ministral 3 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated September 6, 2026

Yes

Yes — Ministral 3 14B at Q4_K_M needs about 10.6 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM) (~1.4 GB spare), at ~27.7 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q4_K_M · Estimated speed: ~27.7 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

Ministral 3 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	30.2 GB	✗ No	—	—	28 GB
Q8_0	17.1 GB	✗ No	—	—	14.9 GB
Q6_K	13.7 GB	✗ No	—	—	11.5 GB
Q5_K_M	12.1 GB	✗ No	—	—	9.9 GB
Q4_K_M	10.6 GB	✓ Yes	8K	~27.7 tok/s	8.5 GB
Q3_K_M	8.1 GB	✓ Yes	16K	~37.2 tok/s	6 GB
Q2_K	6.8 GB	✓ Yes	32K	~45.8 tok/s	4.6 GB

Which Ministral 3 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Ministral 3 14B	10.6 GB	✓ Fits	~27.7 tok/s
Ministral 3 8B	6.8 GB	✓ Fits	~44.9 tok/s
Ministral 3 3B	3.5 GB	✓ Fits	~95.2 tok/s

What to watch out for

Only ~1.4 GB of headroom at Q4_K_M: a longer context or a second application can push this into swapping.
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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