Description

Can enterprises replace costly cloud-hosted models with self-managed, open-weight AI models to reduce AI inference costs? What are the consequences if they don't? A distinguished engineer working on emerging technologies in Red Hat's office of the CTO discusses his team's quest to answer these questions and his outlook on the future of enterprise AI.

Featuring: Stephen Watt, Distinguished Engineer and Vice President, Office of the CTO, Red Hat

In today’s episode, we’ll cover…

• What the 'cloud paradox' and the 'agentic paradox' have in common.
• Reinforcement learning and open weight models as a path to a hybrid cloud AI world.
• The Red Hat research team's experiments with AI automation.
• Tools in development to facilitate hybrid cloud AI inference deployments.

And more!

References:

• Red Hat AI updates target mounting cost, sovereignty worries
• Red Hat, Nvidia tighten integration with AI Factory
• Red Hat amps up open source AI infrastructure pitch

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