LLaVA vs InternVL: best vision model for local use

Vision language models analyzing an image

Vision-language models answer questions about images, and the two open families people actually run are LLaVA and InternVL.

LLaVA is the familiar one: plenty of tutorials, friendly builds for Ollama-style setups, and a large community. If you want to point a model at a screenshot and ask what is wrong, LLaVA-based builds get you there fastest. InternVL tends to score higher on vision benchmarks and handles high-resolution images better, but the builds are less standardized and you will spend more time finding a good quantization.

The hardware note matters: vision models are hungrier than text-only models at the same parameter count. The vision encoder adds video memory (VRAM) overhead, and high-resolution images inflate the context. Budget a few extra gigabytes beyond what you would allocate for a text model of the same size. For most home setups, a 7B-class vision model at 4-bit on a 16GB card is the sweet spot. Budget VRAM for vision models.

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