Can I Run Llama 3.2 Family on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

作者： Jakub Rusinowski · 最后更新： 2026年8月15日

Yes — comfortably

Yes, comfortably — Llama 3.2 3B Instruct at Q8_0 needs about 5.2 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving ~2.8 GB spare and running at ~47 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~47 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)

Llama 3.2 Family 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	8.2 GB	✗ No	—	—	6.4 GB
Q8_0	5.2 GB	✓ Yes	32K	~47 tok/s	3.4 GB
Q6_K	4.4 GB	✓ Yes	32K	~57.3 tok/s	2.6 GB
Q5_K_M	4 GB	✓ Yes	32K	~63.7 tok/s	2.3 GB
Q4_K_M	3.7 GB	✓ Yes	32K	~71.2 tok/s	1.9 GB
Q3_K_M	3.1 GB	✓ Yes	32K	~88.8 tok/s	1.4 GB
Q2_K	2.8 GB	✓ Yes	32K	~102.9 tok/s	1.1 GB

Which Llama 3.2 Family sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Llama 3.2 90B Vision Instruct	57.8 GB	✗ Too large	—
Llama 3.2 11B Vision Instruct	8.5 GB	✗ Too large	—
Llama 3.2 3B Instruct	3.7 GB	✓ Fits	~71.2 tok/s
Llama 3.2 1B Instruct	1.8 GB	✓ Fits	~152.1 tok/s

What to watch out for

2 larger variants of Llama 3.2 Family 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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