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

Superseded model. Llama 3.2 Vision has been superseded by Llama 4. This page is kept for reference; the newer family is a better starting point. View Llama 4 →

作者： Jakub Rusinowski · 最后更新： 2024年9月25日

Yes

Yes — Llama 3.2 Vision 11B at Q3_K_M needs about 6.7 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) (~1.3 GB spare), at ~36.1 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~36.1 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 Vision 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	23.3 GB	✗ No	—	—	21.2 GB
Q8_0	13.4 GB	✗ No	—	—	11.3 GB
Q6_K	10.8 GB	✗ No	—	—	8.7 GB
Q5_K_M	9.7 GB	✗ No	—	—	7.5 GB
Q4_K_M	8.5 GB	✗ No	—	—	6.4 GB
Q3_K_M	6.7 GB	✓ Yes	8K	~36.1 tok/s	4.5 GB
Q2_K	5.6 GB	✓ Yes	16K	~44.2 tok/s	3.5 GB

Which Llama 3.2 Vision sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Llama 3.2 Vision 90B	57.8 GB	✗ Too large	—
Llama 3.2 Vision 11B	8.5 GB	✗ Too large	—

What to watch out for

Only ~1.3 GB of headroom at Q3_K_M: a longer context or a second application can push this into swapping.
Q3_K_M 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 Llama 3.2 Vision 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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