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

Name: LLM Configurator — GPU VRAM Checker
Author: LLM Configurator

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 15 sierpnia 2026

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

The flagship open vision model. Near GPT-4V quality for image understanding. Requires 48GB+ VRAM or multi-GPU setup. Listed in full on the dedicated Llama 3.2 Vision page, which is the canonical entry for this model and carries the install command.

Llama 3.2 90B Vision Instruct 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

Parameters	90 Billion
Context window	128,000
Architecture	Dense + Vision Encoder
Provider	Meta
Licence	Llama Community
Specified at	Q4_K_M
System RAM	128 GB
Record updated	2026-08-15

Licence

Llama 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.

Quant	Weights	VRAM needed	Est. speed	Fit on 24 GB
Q2_K	29.2 GB	33.3 GB	~3 tok/s (est.)	Offloads to system RAM (slow)
Q3_K_M	37.9 GB	42.0 GB	~3 tok/s (est.)	Offloads to system RAM (slow)
Q4_K_M	53.6 GB	57.8 GB	—	Won't fit
Q5_K_M	62.9 GB	67.1 GB	—	Won't fit
Q6_K	72.8 GB	77.0 GB	—	Won't fit
Q8_0	94.3 GB	98.5 GB	—	Won't fit
F16	177.6 GB	181.8 GB	—	Won't fit

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

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Recommended GPU

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

Ujawnienie afiliacyjne: Niektóre odnośniki na tej stronie to linki afiliacyjne — jeśli dokonasz zakupu za ich pośrednictwem, LLM Configurator może otrzymać prowizję bez dodatkowych kosztów dla Ciebie. Jako uczestnik programu Amazon Associates, LLM Configurator zarabia na kwalifikujących się zakupach.

Apple MacBook Pro M5 Pro

64 GB VRAM · 30 W board power

Ceny w 2026 są niestabilne — sprawdź aktualną ofertę.

Sprawdź cenę na Amazon

How to Run Llama 3.2 90B Vision Instruct

Install Ollama, then run:

ollama run llama-3-2

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

Best for: vision, multimodal, enterprise, research.

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

Other Llama 3.2 Family Sizes

Llama 3.2 1B Instruct — about 2 GB VRAM at Q4_K_M
Llama 3.2 3B Instruct — about 3 GB VRAM at Q4_K_M
Llama 3.2 11B Vision Instruct — about 7 GB VRAM at Q4_K_M

Llama 3.2 90B Vision Instruct — Frequently Asked Questions

How much VRAM does Llama 3.2 90B Vision Instruct 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 90B Vision Instruct run on an RTX 4090 (24 GB)?

No. Llama 3.2 90B Vision Instruct 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 90B Vision Instruct locally?

Install Ollama and run `ollama run llama-3-2`. 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 Family come in?

Llama 3.2 1B Instruct (2 GB), Llama 3.2 3B Instruct (3 GB), Llama 3.2 11B Vision Instruct (7 GB), Llama 3.2 90B Vision Instruct (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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