Llama 3.2 Vision 90B — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated September 25, 2024

Model library → Llama 3.2 Vision → Llama 3.2 Vision 90B

Full-power multimodal Llama. Requires high-end hardware (48+ GB VRAM) but matches GPT-4V on most vision benchmarks.

Llama 3.2 Vision 90B needs about 54 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters90 Billion
Context window128,000
ArchitectureDense + Vision Encoder
ProviderMeta
LicenceLlama 3.2 Community
Specified atQ4_K_M
System RAM64 GB
Record updated2024-09-25

Licence

Llama 3.2 Community — commercial use permitted. Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K29.2 GB33.3 GB~3 tok/s (est.)Offloads to system RAM (slow)
Q3_K_M37.9 GB42.0 GB~3 tok/s (est.)Offloads to system RAM (slow)
Q4_K_M53.6 GB57.8 GB—Won't fit
Q5_K_M62.9 GB67.1 GB—Won't fit
Q6_K72.8 GB77.0 GB—Won't fit
Q8_094.3 GB98.5 GB—Won't fit
F16177.6 GB181.8 GB—Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Llama 3.2 Vision 90B VRAM calculator.

Buy This HardwareApple MacBook Pro M5 Pro — 64 GB VRAM · 30 W board powerDeploy in the Cloud NowNVIDIA A100 80GB on RunPod — from $1.39/hr · rate checked 2026-07

or compare on Vast.ai from $0.77/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs Llama 3.2 Vision 90B is the Apple M5 Pro (64 GB).

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Apple MacBook Pro M5 Pro
64 GB VRAM · 30 W board power
2026 prices are volatile — check the current listing.
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How to Run Llama 3.2 Vision 90B

Install Ollama, then run:

ollama run llama3.2-vision:90b

Weights on Hugging Face: meta-llama/Llama-3.2-90B-Vision-Instruct.

Best for: vision, reasoning, research.

Can I Run Llama 3.2 Vision 90B on My GPU?

Other Llama 3.2 Vision Sizes

Llama 3.2 Vision 90B — Frequently Asked Questions

How much VRAM does Llama 3.2 Vision 90B need?
About 54 GB at Q4_K_M — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Llama 3.2 Vision 90B run on an RTX 4090 (24 GB)?
No. Llama 3.2 Vision 90B needs about 54 GB at Q4_K_M, more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.
How do I run Llama 3.2 Vision 90B locally?
Install Ollama and run `ollama run llama3.2-vision:90b`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.
What other sizes does Llama 3.2 Vision come in?
Llama 3.2 Vision 11B (7 GB), Llama 3.2 Vision 90B (54 GB). Every size shares the family's training and licence; the larger ones score higher and need proportionally more memory.

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