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Why is AI inference becoming the new battleground for speed, cost, and real world scalability, and how are companies like Clarifai reshaping the AI stack by optimizing every token and every deployment?

In this episode of Eye on AI, host Craig Smith sits down with Clarifai founder and CEO Matt Zeiler to explore why inference is now more important than training and how a unified compute orchestration layer is changing the way teams run LLMs and agentic systems.

We look at what makes high performance inference possible across cloud, on prem, and edge environments, how to get faster responses from large language models, and how to cut GPU spend without sacrificing intelligence or accuracy. Learn how organizations operate AI systems in regulated industries, how government teams and enterprises use Clarifai to deploy models securely, and which bottlenecks matter most when running long context, multimodal, or high throughput applications.

You will also hear how to optimize your own AI workloads with better token throughput, how to choose the right hardware strategy for scale, and how inference first architecture can turn models into real products. This conversation breaks down the tools, techniques, and design patterns that can help your AI agents run faster, cheaper, and more reliably in production.

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#304 Matt Zeiler: Why Government And Enterprises Choose Clarifai For AI Ops

Eye On A.I.