Optimizing for efficiency with IBM’s Granite 1k3v14

We often judge AI models by leaderboard scores, but what if efficiency matters more? Kate Soule from IBM s us to discuss how Granite AI is rethinking AI at the edge—breaking tasks into smaller, efficient components and co-deg models with hardware. She also shares why AI should prioritize efficiency frontiers over incremental benchmark gains and how seamless model routing can optimize performance.  Featuring: Kate Soule – LinkedIn Chris Benson – Website, GitHub, LinkedIn, X Daniel Whitenack – Website, GitHub, X Links: IBM Granite IBM Granite on Hugging Face IBM Expands Granite Model Family with New Multi-Modal and Reasoning AI Built for the Enterprise  ★ this podcast ★ 6l4w44