Llama 3.2 11B Vision
Llama 3.2 multimodal at 11B. Consumer-tier multimodal predecessor to Llama 4 Scout.
Positioning
Llama 3.2 11B Vision is Meta's consumer-tier multimodal entry in the Llama ecosystem, released under the Llama Community License. With 11 billion dense parameters and a 131,072-token context window, it serves as a predecessor to Llama 4 Scout. This model is designed for vision-language tasks, offering a migration path for operators already invested in the Llama family who need image understanding capabilities without scaling to larger, more expensive models.
Strengths
- Generous context window: 131,072 tokens of context allows processing of long documents or high-resolution image sequences without truncation, a significant advantage for multimodal reasoning tasks.
- Consumer-tier accessibility: At 11B dense parameters, the model fits comfortably on consumer hardware at common quantizations, making local vision-language inference feasible for individual developers and small teams.
- Llama ecosystem compatibility: Operators familiar with Llama tooling, fine-tuning pipelines, and deployment patterns can integrate vision capabilities without switching frameworks or licenses.
- Permissive commercial license: The Llama Community License permits many commercial uses, though operators should review the specific terms for their use case.
Limitations
- No community benchmarks available: We do not yet have independently verified benchmark scores for this model. Operators should treat any published vendor metrics as best-case and validate performance on their own data.
- Dense architecture at 11B: Unlike Mixture-of-Experts models that offer higher parameter counts with lower inference cost, this dense model uses all 11B parameters for every forward pass, limiting throughput compared to MoE alternatives of similar size.
- Vision modality maturity: As an early multimodal release in the Llama line, vision capabilities may not match specialized vision-language models with more training data or later architectural refinements.
- Hardware requirements for full context: While the model fits on consumer GPUs at low quantizations, utilizing the full 131K context window requires substantial KV cache memory (roughly 30-50% overhead), pushing beyond typical 12-24 GB consumer cards at higher precision.
What it takes to run this locally
Quantized model sizes (approximate, disk only):
- FP16: ~22 GB
- Q8_0: ~12 GB
- Q6_K: ~9.1 GB
- Q5_K_M: ~7.8 GB
- Q4_K_M: ~6.2 GB
- Q3_K_M: ~5.4 GB
- Q2_K: ~3.6 GB
Add roughly 30-50% for KV cache and framework overhead at typical context lengths. Deployment class: consumer (single 12-24 GB GPU). At Q4_K_M or lower, the model fits on most modern consumer GPUs (e.g., RTX 3090/4090, 16-24 GB). For FP16 or Q8_0, a workstation GPU (48 GB) or dual consumer cards may be needed.
Should you run this locally?
Yes if you need a consumer-tier multimodal model with a permissive license and are already in the Llama ecosystem. The 131K context window and vision capabilities make it suitable for document analysis, image captioning, and visual Q&A on local hardware.
No if you require state-of-the-art vision-language performance without independent validation, or if your workflow demands higher throughput than a dense 11B model can provide. Consider specialized vision models or MoE architectures for those cases.
Catalog cross-links
- Llama 3.2 3B
- Llama 3.1 8B
- 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, well-supported, with broad runtime coverage. 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; vLLM for multi-user.
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 ✓ excellent. Vision-language support since v0.7+.
- llama.cpp ✓ good. GGUF vision support landed but younger than text-only path.
- 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 | 6.5 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.