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AI with Maribel Lopez: IBM Granite Models with Kate Soule 2i2z1v
11/03/2025
11/03/2025
Episode Summary In this episode, Maribel Lopez interviews Kate Soule, Director of Technical...
Episode Summary
In this episode, Maribel Lopez interviews Kate Soule, Director of Technical Product Management for IBM's Granite products. They discuss IBM's third-generation AI models, their focus on efficiency and enterprise readiness, and the latest advancements including vision capabilities and reasoning features.
Guest
Kate Soule - Director of Technical Product Management for IBM's Granite products
Key Topics & Timestamps
00:04 - Introduction
Maribel introduces the show and Kate Soule
Brief overview of IBM Granite as fit-for-purpose, open-source enterprise AI models
00:48 - What is IBM Granite?
Designed as core building blocks for enterprises building with generative AI
Focus on efficiency with smaller model sizes
Monthly innovation updates to keep pace with rapidly evolving field
02:19 - Understanding AI Reasoning
Explanation of reasoning capabilities in AI models
How allowing models to generate more text at inference time can improve performance
Cost/benefit tradeoffs of reasoning features
03:13 - Enterprise AI Model Selection Criteria
Moving beyond "one model to rule them all" thinking
Importance of fit-for-purpose models
Why smaller models can be customized more easily
Trust and transparency considerations
05:38 - AI Governance and Safety
How to evaluate models for governance requirements
Safety evaluations and benchmarks as table stakes
Systems-based approach to safety with guardrails
IBM's Granite Guardian and protection mechanisms
08:55 - Benefits of Smaller Models
Why size matters: cost, latency, and customization advantages
Smaller models are easier to customize and require less computing power
IBM's transparent approach to training data
10:13 - Future of AI Evaluation
Performance per cost becoming the key evaluation metric
The growing importance of flexibility in model selection
How the "efficient frontier" between cost and performance will differentiate providers
12:41 - IBM's Vision Models
IBM's pragmatic enterprise focus for multimodal capabilities
Vision understanding (image in, text out) for practical business use cases
Specialization for documents, charts, and dashboards
Delivering powerful capabilities in only 2 billion parameters
15:25 - Understanding Model Size Context
Evolution from millions to billions of parameters
Practical considerations of deploying different-sized models
Finding the right cost-benefit trade-off for specific use cases