Can I Run Yi 1.5 Family on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?

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Written by Jakub Rusinowski · Last updated May 13, 2024

Yes, but it is tight

Yes, but it is tight — Yi 1.5 9B Chat at Q8_0 needs about 11 GB of the 12 GB usable on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM), leaving only ~1 GB before the runtime starts swapping. Expect ~26 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: medium · Recommended quantization: Q8_0 · Estimated speed: ~26 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

Yi 1.5 Family 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	19.3 GB	✗ No	—	—	17.7 GB
Q8_0	11 GB	✓ Yes	16K	~26 tok/s	9.4 GB
Q6_K	8.8 GB	✓ Yes	16K	~32.7 tok/s	7.2 GB
Q5_K_M	7.9 GB	✓ Yes	16K	~37.2 tok/s	6.3 GB
Q4_K_M	6.9 GB	✓ Yes	16K	~42.6 tok/s	5.3 GB
Q3_K_M	5.4 GB	✓ Yes	16K	~56.6 tok/s	3.8 GB
Q2_K	4.5 GB	✓ Yes	16K	~69 tok/s	2.9 GB

Which Yi 1.5 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Yi 1.5 34B Chat	23.6 GB	✗ Too large	—
Yi 1.5 9B Chat	6.9 GB	✓ Fits	~42.6 tok/s

What to watch out for

Only ~1 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
1 larger variant of Yi 1.5 Family 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.

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