Can I Run OLMo 2 on RTX 3060 12 GB Desktop (12 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated November 26, 2024

Yes, but it is tight

Yes, but it is tight — OLMo 2 7B Instruct at Q6_K 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 ~30.9 tok/s (estimated), with room for about 4,096 tokens of context.

Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~30.9 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

OLMo 2 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	19.7 GB	✗ No	—	—	14.6 GB
Q8_0	12.9 GB	✗ No	—	—	7.8 GB
Q6_K	11.1 GB	✓ Yes	4K	~30.9 tok/s	6 GB
Q5_K_M	10.3 GB	✓ Yes	4K	~34.1 tok/s	5.2 GB
Q4_K_M	9.5 GB	✓ Yes	4K	~37.7 tok/s	4.4 GB
Q3_K_M	8.2 GB	✓ Yes	4K	~46 tok/s	3.1 GB
Q2_K	7.5 GB	✓ Yes	4K	~52.4 tok/s	2.4 GB

Which OLMo 2 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
OLMo 2 13B Instruct	15.8 GB	✗ Too large	—
OLMo 2 7B Instruct	9.5 GB	✓ Fits	~37.7 tok/s

What to watch out for

Only ~0.9 GB of headroom at Q6_K: a longer context or a second application can push this into swapping.
Context is capped at about 4,096 tokens before memory runs out, which is short for document or agent work.
1 larger variant of OLMo 2 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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