Not long ago, Ollama announced the launch of a brand-new multimodal AI engine, which was developed independently of the original llama.cpp framework. This marks an important step for the company in the field of artificial intelligence. This engine is built using the Golang programming language, designed to significantly improve the accuracy of local inference and enhance the capability of processing large-scale images.
The highlights of the new engine include the introduction of image processing metadata, KVCache optimization, and image caching functionality. These innovations have achieved breakthroughs in memory management and resource utilization efficiency, ensuring that AI models operate more efficiently. This is particularly important for complex models like Llama4Scout, which require handling large amounts of data, enabling more precise results in less time.
Source Note: Image generated by AI, licensed by MidJourney
In addition, the new engine also supports advanced technologies such as chunked attention mechanisms and 2D rotation embeddings. These features enable the engine to flexibly handle different types of data inputs, whether images or text, maintaining high efficiency and accuracy during processing. The Ollama team stated that this flexibility is one of their core goals in developing this engine, aiming to provide users with stronger AI application capabilities.
Ollama's move not only enhances the performance of local AI inference but also makes large-scale image processing more efficient, opening up new possibilities for developers and researchers. As technology continues to advance, Ollama's multimodal AI engine will play an increasingly important role in future applications, and we look forward to seeing its greater potential in practical use cases.