Ollama's new engine enhances support for multimodal models, focusing on improved reliability, accuracy, and future modality support (speech, image/video generation).
Key features include:
- Model Modularity: Each model is self-contained, simplifying integration and improving reliability.
- Accuracy: Metadata is added during image processing to enhance accuracy, especially with large images and batch processing.
- Memory Management: Image caching, memory estimation, and KV cache optimizations improve performance and efficiency.
Use cases include general multimodal understanding (Llama 4, Gemma 3), document scanning (Qwen 2.5 VL), and future support for longer context sizes and tool calling.
