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

Written by Jakub Rusinowski · Last updated April 28, 2025

Yes, but it is tight

Yes, but it is tight — Qwen 3 14B at Q4_K_M needs about 11.1 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM), leaving only ~0.9 GB before the runtime starts swapping. Expect ~26.4 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: medium · Recommended quantization: Q4_K_M · Estimated speed: ~26.4 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

Qwen 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	31.7 GB	✗ No	—	—	29.6 GB
Q8_0	17.9 GB	✗ No	—	—	15.7 GB
Q6_K	14.3 GB	✗ No	—	—	12.1 GB
Q5_K_M	12.6 GB	✗ No	—	—	10.5 GB
Q4_K_M	11.1 GB	✓ Yes	8K	~26.4 tok/s	8.9 GB
Q3_K_M	8.5 GB	✓ Yes	16K	~35.6 tok/s	6.3 GB
Q2_K	7 GB	✓ Yes	32K	~44 tok/s	4.9 GB

Which Qwen 3 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen 3 235B-A22B (MoE)	144.3 GB	✗ Too large	—
Qwen 3 32B	22.8 GB	✗ Too large	—
Qwen 3 30B-A3B (MoE)	20 GB	✗ Too large	—
Qwen 3 14B	11.1 GB	✓ Fits	~26.4 tok/s
Qwen 3 8B	7 GB	✓ Fits	~43.8 tok/s

What to watch out for

Only ~0.9 GB of headroom at Q4_K_M: a longer context or a second application can push this into swapping.
3 larger variants of Qwen 3 do 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.

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