Can I Run OLMo 2 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

作者： Jakub Rusinowski · 最后更新： 2024年11月26日

Yes, but it is tight

Yes, but it is tight — OLMo 2 7B Instruct at Q2_K needs about 7.5 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.5 GB before the runtime starts swapping. Expect ~40.8 tok/s (estimated), with room for about 4,096 tokens of context.

Confidence: medium · Recommended quantization: Q2_K · Estimated speed: ~40.8 tok/s

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RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) — what it gives a model

Usable memory for models	8 GB
Memory bandwidth	272 GB/s
Form factor	Laptop
Operating system	Windows or Linux
Memory upgradeable	Yes
Price	$1,099 (lib/data/laptops.ts (street price), checked 2026-07-06)

OLMo 2 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM): memory by quantization

Quant	Memory needed	Fits 8 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	✗ No	—	—	6 GB
Q5_K_M	10.3 GB	✗ No	—	—	5.2 GB
Q4_K_M	9.5 GB	✗ No	—	—	4.4 GB
Q3_K_M	8.2 GB	✗ No	—	—	3.1 GB
Q2_K	7.5 GB	✓ Yes	4K	~40.8 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	✗ Too large	—

What to watch out for

Only ~0.5 GB of headroom at Q2_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.
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.
2 larger variants of OLMo 2 do not fit and would need CPU offload or different hardware.

RTX 4060 laptop limitations

8 GB VRAM limits you to 7–8B models at Q4 with a short context.
System RAM is usually upgradeable on this class of laptop even though VRAM is not.

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.
8 GB of VRAM on the NVIDIA GeForce RTX 4060 at 272 GB/s.
16 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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