Can I Run Llama 3.2 Vision on RTX 3090 Desktop (24 GB VRAM, 64 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Superseded model. Llama 3.2 Vision has been superseded by Llama 4.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 →

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 25 września 2024

Yes — comfortably

Yes, comfortably — Llama 3.2 Vision 11B at Q8_0 needs about 13.4 GB of the 24 GB usable on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM), leaving ~10.6 GB spare and running at ~52 tok/s (estimated), with room for about 65,536 tokens of context.

Confidence: high · Recommended quantization: Q8_0 · Estimated speed: ~52 tok/s

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RTX 3090 Desktop (24 GB VRAM, 64 GB RAM) — what it gives a model

Usable memory for models	24 GB
Memory bandwidth	936 GB/s
Form factor	Desktop
Operating system	Windows or Linux
Memory upgradeable	Yes

Llama 3.2 Vision on RTX 3090 Desktop (24 GB VRAM, 64 GB RAM): memory by quantization

Quant	Memory needed	Fits 24 GB?	Max context	Est. speed	Download
F16	23.3 GB	✓ Yes	8K	~29.9 tok/s	21.2 GB
Q8_0	13.4 GB	✓ Yes	64K	~52 tok/s	11.3 GB
Q6_K	10.8 GB	✓ Yes	64K	~64.2 tok/s	8.7 GB
Q5_K_M	9.7 GB	✓ Yes	64K	~71.9 tok/s	7.5 GB
Q4_K_M	8.5 GB	✓ Yes	64K	~81.2 tok/s	6.4 GB
Q3_K_M	6.7 GB	✓ Yes	64K	~103.8 tok/s	4.5 GB
Q2_K	5.6 GB	✓ Yes	64K	~122.5 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	✓ Fits	~81.2 tok/s

What to watch out for

1 larger variant of Llama 3.2 Vision does not fit and would need CPU offload or different hardware.

RTX 3090 desktop limitations

The cheapest route to 24 GB of VRAM, and the standard used-market recommendation for local LLMs.
Older architecture: no FP8 acceleration, and higher idle power than a current card.

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.
24 GB of VRAM on the NVIDIA GeForce RTX 3090 at 936 GB/s.
64 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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