Llama 3.2 11B Vision Instruct — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated August 15, 2026

Model library → Llama 3.2 Family → Llama 3.2 11B Vision Instruct

Meta's first open multimodal model. Understands images and text together — describe photos, analyze charts, read documents. Runs on 8GB+ VRAM GPUs. 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 11B Vision Instruct needs about 7 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

Parameters11 Billion
Context window128,000
ArchitectureDense + Vision Encoder
ProviderMeta
LicenceLlama Community
Specified atQ4_K_M
System RAM16 GB
Record updated2026-08-15

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.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K3.5 GB5.6 GB~129 tok/s (est.)Fits comfortably
Q3_K_M4.5 GB6.7 GB~110 tok/s (est.)Fits comfortably
Q4_K_M6.4 GB8.5 GB~86 tok/s (est.)Fits comfortably
Q5_K_M7.5 GB9.7 GB~77 tok/s (est.)Fits comfortably
Q6_K8.7 GB10.8 GB~68 tok/s (est.)Fits comfortably
Q8_011.3 GB13.4 GB~55 tok/s (est.)Fits comfortably
F1621.2 GB23.3 GB~32 tok/s (est.)Tight fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Llama 3.2 11B Vision Instruct VRAM calculator.

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board powerDeploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

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Recommended GPU

The cheapest catalogued GPU that runs Llama 3.2 11B Vision Instruct is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB
10 GB VRAM · 150 W board power
2026 prices are volatile — check the current listing.
Check price on Amazon

How to Run Llama 3.2 11B Vision Instruct

Install Ollama, then run:

ollama run llama-3-2

Weights on Hugging Face: meta-llama/Llama-3.2-11B-Vision-Instruct.

Best for: vision, multimodal, document analysis, chat.

Can I Run Llama 3.2 11B Vision Instruct on My GPU?

Other Llama 3.2 Family Sizes

Llama 3.2 11B Vision Instruct — Frequently Asked Questions

How much VRAM does Llama 3.2 11B Vision Instruct need?
About 7 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 11B Vision Instruct run on an RTX 4090 (24 GB)?
Yes. Llama 3.2 11B Vision Instruct needs about 7 GB at Q4_K_M, inside a 24 GB card, at an estimated 86 tokens/sec.
How do I run Llama 3.2 11B 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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