Llama 3.2 Vision — local AI model by Meta
Written by Jakub Rusinowski · Last updated
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.
Variants
The smallest Llama 3.2 Vision variant needs about 7 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache.
| Model | VRAM at Q4 | VRAM | Context | Run it |
|---|---|---|---|---|
| Llama 3.2 Vision 11B → 11B | ~7.2 GB | 128,000 | ollama run llama3.2-vision:11b | |
| Llama 3.2 Vision 90B → 90B | ~54.4 GB | 128,000 | ollama run llama3.2-vision:90b |
Memory is quantized weights plus overhead at Q4_K_M, from the same engine as the GPU & VRAM checker.
How to run Llama 3.2 Vision locally
Install Ollama, then pull the tag.
ollama run llama3.2-vision:11bPick a size above for its own VRAM figure, speed estimate and install command.
Licence
Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.
Applies to: Llama 3.2 Vision 11B, Llama 3.2 Vision 90BRecommended GPU
The cheapest catalogued GPU that runs Llama 3.2 Vision locally (min 7 GB VRAM) is the Intel Arc B570 (10 GB).
Can I run Llama 3.2 Vision on my GPU?
- Llama 3.2 Vision on AMD Radeon RX 9060 XT 8GB
- Llama 3.2 Vision on AMD Ryzen AI Max+ 395
- Llama 3.2 Vision on Apple M1 Ultra
- Llama 3.2 Vision on Apple M2 Max
- Llama 3.2 Vision on Apple M4 Max
- Llama 3.2 Vision on Apple M5 Max
- Llama 3.2 Vision on Intel Arc B570
- Llama 3.2 Vision on Intel Arc B580
- Llama 3.2 Vision on NVIDIA A100 80GB (PCIe)
- Llama 3.2 Vision on NVIDIA DGX Spark
- Llama 3.2 Vision on NVIDIA GeForce RTX 3060 (12GB)
- Llama 3.2 Vision on NVIDIA GeForce RTX 3070
- Llama 3.2 Vision on NVIDIA GeForce RTX 3070 Ti
- Llama 3.2 Vision on NVIDIA GeForce RTX 3080 (10GB)
- Llama 3.2 Vision on NVIDIA GeForce RTX 4060
- Llama 3.2 Vision on NVIDIA GeForce RTX 4070
- Llama 3.2 Vision on NVIDIA GeForce RTX 4070 Super
- Llama 3.2 Vision on NVIDIA GeForce RTX 4070 Ti
- Llama 3.2 Vision on NVIDIA GeForce RTX 5060
- Llama 3.2 Vision on NVIDIA GeForce RTX 5060 Ti 8GB
- Llama 3.2 Vision on NVIDIA GeForce RTX 5070
- Llama 3.2 Vision on NVIDIA H100 80GB (PCIe)
- Llama 3.2 Vision on NVIDIA RTX PRO 6000 Blackwell
Llama 3.2 Vision — frequently asked questions
How much VRAM does Llama 3.2 Vision need?
Llama 3.2 Vision needs about 7 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Llama 3.2 Vision 11B (7 GB, Q4_K_M); Llama 3.2 Vision 90B (54 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.