Ollama vision and multimodal usage
Parent: Ollama server ops, scheduling, GPU, sizing and desktop · Published reference · snapshot 2026-10-14
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Source-anchored research for Ollama vision and multimodal usage. Run status: COMPLETED.
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Problems, failure modes and limitations
- Slow local vision models may need a longer client-side image timeout than cloud models; the client timeout is separate from the Ollama HTTP request guard. (low confidence) [source]
Comparisons and alternatives
- Ollama's multimodal support moved to a new in-house engine (announced 2025-05-15) that calls GGML directly from Go instead of relying on llama.cpp for model support. Each model is self-contained and carries its own projection layer, so a failure stays local to that model. (high confidence) [source]
Facts and statements
- Images are passed as an `images` array. On /api/chat it sits on each message object; on /api/generate it is a top-level field. The REST API expects base64-encoded data; the official SDKs also accept file paths, URLs or raw bytes. (high confidence) [source]
- The CLI accepts an image path inline in the prompt, e.g. `ollama run gemma4 ./image.png what is in this image?`. (medium confidence) [source]
- Launch models on the new engine included Llama 4 (Scout/Maverick), Gemma 3, Qwen 2.5 VL and Mistral Small 3.1. Current Ollama docs use gemma4 as the vision example. (medium confidence) (verified as of 2026-10-14) [source]
- The engine caches processed images so later prompts that reuse them run faster. Gemma 3 uses sliding-window attention, which frees KV memory for longer context or higher concurrency; Llama 4 uses chunked attention. (low confidence) [source]
- Large images produce many tokens that can exceed the batch size. Splitting image embeddings at the wrong point degrades output quality. Incompletely implemented sliding or chunked attention can still run but give degraded output on long sequences. (low confidence) [source]
- Images consume context tokens. With Ollama's default 4096-token context, glm-ocr returned empty output; raising num_ctx to 10240 or more produced output (reported 2026-02-05). Ollama silently truncates over-long context, so the right value is hard to guess. (medium confidence) (verified as of 2026-02-05) [source]
- In that report, setting OLLAMA_CONTEXT_LENGTH had no effect. The working path was `/set parameter num_ctx N` in the REPL followed by `/save <name>`, since `/set` does not persist after exit. (low confidence) (verified as of 2026-02-05) [source]
- On constrained hardware, Ollama may try to allocate a vision model's full advertised context and crash. OpenClaw's docs advise capping num_ctx in model params (example 2048) and, optionally, a short keep_alive. (low confidence) (verified as of 2026-10-14) [source]
- /api/show returns a `capabilities` list that includes `vision` for image-capable models. Clients such as OpenClaw use it to decide whether to send images. (medium confidence) [source]
- /api/embed accepts input objects with `text`, `image` or `audio` keys (base64) on models that support multimodal embeddings. Video is not accepted. (low confidence) (verified as of 2026-10-14) [source]
- The official docs and API reference state no maximum image count, file size or accepted format list for vision input. (low confidence) (verified as of 2026-10-14) [source]
Children
- No children recorded.