#335 Sriram Raghavan: Why IBM Is Betting Everything on Small AI Models
From Eye On A.I. by Craig S. Smith
April 19, 2026 · 1h 0m · Episode 335
About this episode
Craig Smith interviews Sriram Raghavan about IBM's strategy on small AI models and their implications for enterprise AI.
Why IBM Is Betting Everything on Small AI Models In this episode of Eye on AI, Craig Smith sits down with Sriram Raghavan, Vice President of AI at IBM Research, to explore one of the most important debates in enterprise AI right now. Do you actually need a massive model to get world class results? IBM's answer is no, and Sriram breaks down exactly why. Sriram explains why IBM chose to train its Granite models directly using reinforcement learning rather than distilling from larger models like most of the industry. The reason goes beyond performance. It comes down to data lineage, safety alignment, and a belief that small, efficient models are the only sustainable path for enterprises running AI across hybrid cloud environments. We get into the full technical stack behind that bet. How data quality has replaced model size as the real competitive advantage. Why parameter count is becoming the wrong metric entirely. How IBM's inference time scaling techniques allow an 8 billion parameter model to match the performance of GPT-4o and Claude 3.5 on code and math benchmarks. And why IBM is pioneering a new concept called Generative Computing, which treats AI models not as prompt…
People in this episode
Host: Craig Smith
Guest: Sriram Raghavan
Topics covered
- enterprise AI
- small AI models
- reinforcement learning
- data quality
- inference time scaling
- Generative Computing
Keywords
- AI models
- data lineage
- safety alignment
- hybrid cloud
- parameter count
- continuous learning
- agent orchestration
Mentioned in this episode
Organizations: IBM, IBM Research
Products: Granite models, GPT-4o, Claude 3.5
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