Llama 3.2 11B Vision
Llama 3.2 multimodal at 11B. Consumer-tier multimodal predecessor to Llama 4 Scout.
Overview
Llama 3.2 multimodal at 11B. Consumer-tier multimodal predecessor to Llama 4 Scout.
Execution notes
Operator notes
Llama 3.2 11B Vision is the consumer-tier multimodal Llama from September 2024 — not the latest (Llama 4 Scout is sharper) but stable and available on Ollama. The right pick when you want Meta's multimodal lineage in a smaller hardware envelope and don't need frontier-tier visual reasoning.
The honest framing in May 2026: this model has been surpassed by Pixtral 12B and Qwen 2.5-VL 7B on most visual reasoning benchmarks at the same size class. It remains operationally useful because of Llama-ecosystem deployment infrastructure already tuned for it.
Deployment notes
Fits 12GB VRAM at Q4_K_M comfortably; ideal for the 16GB-VRAM consumer tier. Pairs with Ollama for solo developer setups.
The /stacks/local-vision-model recipe defaults to Llama 4 Scout at the workstation tier; for the consumer tier, Pixtral 12B usually wins. Llama 3.2 11B Vision is the safe Llama-ecosystem migration path when team infrastructure is Llama-aligned.
Runtime compatibility
- Ollama ✓ excellent. Native vision support; one-line pull.
- vLLM ✗ removed. Its model registry lists this architecture as last supported in v0.10.2.
- llama.cpp ✗ not listed among the vision models in its multimodal docs.
- MLX-LM ✓ partial. Apple Silicon multimodal path is improving but Pixtral has stronger MLX integration.
- TensorRT-LLM ✓ partial. Multimodal compile path exists; recompile friction is high.
Best use cases
- Llama-ecosystem migration — when team infrastructure is already tuned for Llama and you need multimodal capability.
- Consumer-tier image Q&A at 12GB+ VRAM — fits without the 24GB+ workstation requirement of larger VLMs.
- Educational / research deployments — Llama Community License is permissive enough for most academic uses.
- Document Q&A on text-heavy documents — solid OCR-then-reasoning capability for the size class.
When to use a different model
- Latest multimodal: Llama 4 Scout — datacenter-tier; significantly stronger visual reasoning.
- Apache 2.0 license required: Pixtral 12B or Qwen 2.5-VL 7B — clean Apache 2.0.
- Frontier-tier vision: Llama 3.2 90B Vision — same family, datacenter-tier.
- OCR-first workloads: dedicated OCR models (Florence-2, MiniCPM-V) often beat general VLMs at text extraction.
- Apple Silicon multimodal: Pixtral 12B has stronger MLX integration today.
- Smaller / edge tier: Moondream 2 at 1.9B; Qwen 2.5-VL 7B.
Failure modes specific to this model
- Older release — community has moved on. Pixtral 12B and Qwen 2.5-VL 7B both surpass it on most benchmarks. Don't deploy this for new greenfield projects unless Llama-ecosystem alignment is a hard requirement.
- Vision tokenization is the 2024 generation. Newer VLMs use more efficient vision encoders; Llama 3.2 Vision spends more tokens per image than newer competitors.
- Llama Community License usage restrictions for very large companies — verify your scale tolerates the license.
Going deeper
- Llama 3.2 90B Vision — datacenter-tier sibling
- Llama 4 Scout — the current Llama multimodal
- Pixtral 12B — competitive consumer-tier alternative
- Qwen 2.5-VL 7B — competitive consumer-tier alternative
- /stacks/local-vision-model — multimodal deployment context
Family & lineage
How this model relates to others in its lineage. Family members share architecture and training-data roots; parent / children edges record direct distillation or fine-tune relationships.
Strengths
- Consumer-tier multimodal
- Llama Community License
Weaknesses
- Older release — Llama 4 Scout / Pixtral / Qwen 2.5-VL are sharper
Quantization variants
Each quantization trades model quality for file size and VRAM. Q4_K_M is the most popular starting point.
| Quantization | File size | VRAM required |
|---|---|---|
| Q4_K_M | 7.8 GB | 9 GB |
Get the model
HuggingFace
Original weights
Source repository — direct quantization required.
Hardware that runs this
Cards with enough VRAM for at least one quantization of Llama 3.2 11B Vision.
Models worth comparing
Same parameter band, plus what's one tier above and below — so you can decide what actually fits your hardware.
Frequently asked
What's the minimum VRAM to run Llama 3.2 11B Vision?
Can I use Llama 3.2 11B Vision commercially?
What's the context length of Llama 3.2 11B Vision?
Does Llama 3.2 11B Vision support images?
Source: huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct
Reviewed by RunLocalAI Editorial. See our editorial policy for how we research and verify model claims.
Related — keep moving
Verify Llama 3.2 11B Vision runs on your specific hardware before committing money.