Llama 3.2 Vision 11B
By Meta · United States
Updated 2026-07-13
Overview
Meta's first official multimodal Llama. An 11B vision-language model built on Llama 3.1 8B with added image adapters and a 128k text context.
When to pick this model
- OCR and document understanding on a consumer GPU
- Image captioning and description pipelines
- Chart and graph analysis
- Mixed text-and-image RAG workloads
- Llama ecosystem deployments needing vision
VRAM requirements by quantization
| Quantization | VRAM required |
|---|---|
| Q4_K_M (recommended) | 8 GB |
| Q5_K_M | 10 GB |
| Q8_0 | 14 GB |
| FP16 (no quantization) | 24 GB |
VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.
In practice, Llama 3.2 Vision 11B fits an 8 GB consumer card at Q4_K_M (8 GB). Stepping up to Q8_0 nearly doubles the footprint to 14 GB, and unquantized FP16 weights take 24 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.
Without a GPU, Llama 3.2 Vision 11B needs roughly 14 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 6 tokens/sec on entry-level GPUs, on the order of 22 tokens/sec on a mid-range card, and up to 55 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.
What hardware do you need
The table below matches Llama 3.2 Vision 11B to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.
| GPU memory | Example cards | Best fit for Llama 3.2 Vision 11B |
|---|---|---|
| 8 GB | RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB | Q4_K_M (8 GB used) |
| 12 GB | RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop | Q5_K_M (10 GB used) |
| 16 GB | RTX 5080, RTX 4080 Super, Radeon RX 9070 XT | Q8_0 (14 GB used) |
| 24 GB | RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop | FP16 (24 GB used) |
| 32 GB | RTX 5090 | FP16 (24 GB used) |
Which GPU should you buy to run Llama 3.2 Vision 11B?
To run Llama 3.2 Vision 11B locally at Q4, you need ~8 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).
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Published benchmark scores
| Benchmark | Score |
|---|---|
| MMMU | 50.7 |
| DocVQA | 88.4 |
Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.
To put Llama 3.2 Vision 11B in context: its MMMU score of 50.7 ranks #8 of the 8 catalog models with a published MMMU result (catalog median 62.8). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.
Strengths
- 128k text context with image input
- Strong OCR and image description
- Built on the well-supported Llama 3 base
- First-party Meta multimodal release
Limitations
- Vision quality trails Qwen2-VL and LLaVA-OneVision
- Subject to Llama Community license terms
- No video understanding
- Image inputs add significant VRAM overhead
Typical workloads
In our catalog grid, Llama 3.2 Vision 11B is filed under Vision, Scanned Documents — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: vision-language work — screenshots, charts, scanned documents.
The 128k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. It ships under the Llama 3 Community license — commercial use is generally possible but read the specific terms before embedding it in a product.
Architecture & training
Architecture: Dense · 11B · vision cross-attention · CLIP encoder · Llama 3.2
Training: Llama 3.1 8B + vision adapters. First official Meta vision model.
A solid Llama-family vision model — but Qwen2-VL is the better open-weight choice when license terms allow.
Quick start
ollama run llama3.2-vision:11bOr use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.
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Frequently asked questions
How much VRAM does Llama 3.2 Vision 11B need?
At the recommended Q4_K_M quantization, Llama 3.2 Vision 11B needs about 8 GB of VRAM. Q8_0 takes 14 GB, and unquantized FP16 weights take 24 GB.
Can Llama 3.2 Vision 11B run without a GPU?
Yes — with roughly 14 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.
What context window does Llama 3.2 Vision 11B support?
Llama 3.2 Vision 11B supports a 128k-token context window (131,072 tokens).
Can I use Llama 3.2 Vision 11B commercially?
Llama 3.2 Vision 11B ships under the Llama 3 Community license. Commercial use is generally permitted subject to its terms — review the license text before shipping a product.
How fast is Llama 3.2 Vision 11B on consumer hardware?
Our compatibility engine estimates on the order of 22 tokens/sec on a mid-range GPU and up to 55 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.
Which quantization of Llama 3.2 Vision 11B should I download first?
Start with Q4_K_M (8 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q4_K_M.