The main reason the presenter finds smaller AI models more exciting is that they represent tangible engineering progress, being lighter, more efficient, and closer to running on everyday hardware, unlike the incremental benchmark improvements of giant models.
IBM's Granite 4.0 models differ from traditional transformer models by mixing transformer layers with Mamba layers. This hybrid architecture allows them to handle long contexts more efficiently, enabling them to process and retain information from very large inputs like entire documents or extensive chat histories without forgetting earlier parts.
This video introduces IBM's Granite 4.0 family of AI models, highlighting their compact size, efficiency, and ability to run locally. The presenter contrasts these "tiny" models with the increasingly less exciting "giant" AI models, emphasizing the engineering progress in creating smaller, faster AI. The video demonstrates the capabilities of Granite 4.0, including its hybrid architecture combining transformers and Mamba layers for efficient long-context handling, its tool-calling functionality, and its potential for offline applications like a browser-based code completion assistant.
The practical advantages of running AI models locally and offline, as demonstrated with the coding assistant app, include:
The features of the Granite 4.0 models that make them suitable for deployment in sensitive environments like finance or healthcare are: