Local Multi-Modal Vision Stack

Best for

Builders analyzing screenshots, diagrams, product images, scanned documents, or video frames with a local model — without sending images to cloud vision APIs.

Core tools

• Ollama
• Open WebUI
• LM Studio
• llama.cpp

Recommended models

• Llama 4 Scout (natively multimodal, Ollama IQ2 ~38 GB) — best for 40+ GB VRAM systems
• Gemma 3 12B / 27B — vision-capable across image and text with 128K context
• LLaVA 1.6 (34B or 13B) — mature vision Q&A model, well-supported in Ollama
• MiniCPM-V 2.6 (8B) — compact vision model, good for image description at 8 GB VRAM

Hardware notes

Vision models need extra VRAM beyond the base LLM weights: budget 2–4 GB above standard text-only requirements. MiniCPM-V 2.6 fits in 8 GB; LLaVA 13B needs ~12 GB; Gemma 3 27B needs ~20 GB; Llama 4 Scout needs 40+ GB.

Setup steps

1. Install Ollama and pull a vision-capable model: ollama pull llava:13b or ollama pull gemma3:27b
2. Verify the model loads: ollama run llava:13b (exit with /bye)
3. Open Open WebUI (localhost:3000) — drag and drop images directly into the chat input.
4. Test with a simple image: a screenshot, a chart, or a product photo with a question like "What does this diagram show?"
5. For document images (PDFs scanned as images), combine with a pipeline: pdf-to-image → vision model → summarize.
6. For batch image analysis, call the Ollama API directly with base64-encoded images in the messages array.

Trade-offs

Local vision models are slower and less capable than GPT-4o or Claude 3.5 Sonnet for complex image tasks. They are strong for structured extraction, classification, and description. For nuanced reasoning over images, frontier APIs may produce better results.

Alternatives

• Use Moondream for a very small (1.8B) fast image captioning model on low-VRAM hardware.
• Use cloud vision APIs when accuracy on complex images is more important than privacy.
• Use Docling for PDF-native parsing when document structure matters more than raw image understanding.

Related resources

Not sure if your PC has enough VRAM for this workflow?

FAQ