Llama 3.2 Vision — Local AI Model by Meta

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

作者： Jakub Rusinowski · 最后更新： 2026年7月10日

Meta's multimodal extension of Llama 3.2, adding a vision encoder for image understanding. The 11B version runs on a single 8–12 GB VRAM GPU. Supports image analysis, OCR, chart reading, and visual Q&A alongside text generation.

Hardware Requirements

Llama 3.2 Vision 11B	Min 8 GB VRAM · Q4_K_M · 128,000 ctx · `ollama run llama3.2-vision:11b`
Llama 3.2 Vision 90B	Min 48 GB VRAM · Q4_K_M · 128,000 ctx · `ollama run llama3.2-vision:90b`

Recommended GPU

The cheapest GPU that runs Llama 3.2 Vision locally (min 8 GB VRAM) is the Intel Arc B570 (10 GB).

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Intel Arc B570 10GB

首发建议零售价：$219

2026年价格波动较大——请以当前商品页价格为准。

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How to Run Locally

Install Ollama then run: ollama run llama3.2-vision:11b

Minimum VRAM: 8 GB. For best results use Q4_K_M quantization.

Llama 3.2 Vision — Frequently Asked Questions

How much VRAM does Llama 3.2 Vision need?

Llama 3.2 Vision needs about 8 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Llama 3.2 Vision 11B (8 GB, Q4_K_M); Llama 3.2 Vision 90B (48 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.

Can I run Llama 3.2 Vision on an RTX 4090 (24 GB)?

Yes — Llama 3.2 Vision runs on an RTX 4090 (24 GB) and other 24 GB cards such as the RTX 3090. Smaller variants also fit comfortably on 8–16 GB GPUs at Q4_K_M.

What quantization should I use for Llama 3.2 Vision?

Q4_K_M is the best balance of quality and VRAM for Llama 3.2 Vision in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.

How do I run Llama 3.2 Vision with Ollama?

Install Ollama, then run: ollama run llama3.2-vision:11b. This downloads Llama 3.2 Vision and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.