Can I Run Llama 3.2 Vision on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

Written by Jakub Rusinowski · Last updated September 25, 2024

RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) — what it gives a model

Llama 3.2 Vision on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM): memory by quantization

Which Llama 3.2 Vision sizes fit

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 5080 desktop limitations

• 16 GB VRAM is the binding constraint, not compute — a slower 24 GB card runs strictly more models.

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.
• 16 GB of VRAM on the NVIDIA GeForce RTX 5080 at 960 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.

FAQ

Can I run Llama 3.2 Vision on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

Yes — Llama 3.2 Vision 11B at Q8_0 needs about 13.4 GB of the 16 GB usable on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM) (~2.6 GB spare), at ~53.1 tok/s (estimated), with room for about 16,384 tokens of context.

Which quantization of Llama 3.2 Vision should I use on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

Q8_0 — it needs about 13.4 GB of the 16 GB available, downloads as roughly 11.3 GB, and runs at an estimated 53.1 tokens/sec with up to 16K of context.

What limits Llama 3.2 Vision on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)?

Nothing binding — the model fits with headroom and generates at a usable speed on this hardware.

Which runtime should I use?

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

Other Computers

• Llama 3.2 Vision on RTX 4090 desktop
• Llama 3.2 Vision on RTX 5090 desktop
• Llama 3.2 Vision on Mac Studio M3 Ultra 256 GB
• Llama 3.2 Vision on RTX 3090 desktop

Other Models on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)

• Ministral on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
• Mistral Family on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
• Mistral Small 3.1 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
• Nemotron Cascade 2 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)
• OLMo 2 on RTX 5080 Desktop (16 GB VRAM, 32 GB RAM)

Llama 3.2 Vision on GPUs

• Llama 3.2 Vision on NVIDIA GeForce RTX 5070
• Llama 3.2 Vision on NVIDIA GeForce RTX 5060 Ti 8GB
• Llama 3.2 Vision on NVIDIA GeForce RTX 5060
• Llama 3.2 Vision on NVIDIA GeForce RTX 4070 Ti

What This Model Is Good At

• Best local LLMs for general assistant
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Model & Tools

• Llama 3.2 Vision model page
• VRAM calculator
• Check your own hardware

← Can I Run It? | Llama 3.2 Vision model page | Check your hardware