AI Inferencing Everywhere: Scaling Enterprise AI from Core to Edge
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As AI moves into production, enterprises must solve for distributed execution across core and edge environments. This conversation explores how infrastructure is evolving to support scalable, real-time AI inferencing.
AI is moving out of the lab and into real-world environments, and the challenge is no longer building models, it’s running them everywhere.
At NVIDIA GTC, hosts Patrick Moorhead and Daniel Newman sit down with Vlad Rozanovich of Lenovo and Jon Alexander of Akamai to explore how distributed infrastructure is enabling enterprise AI inferencing from the core to the edge.
The conversation unpacks how enterprises are shifting from centralized AI architectures to highly distributed environments where performance, consistency, and security must hold across locations. Through Lenovo’s collaboration with Akamai, AI workloads are being deployed on infrastructure that spans data centers to edge locations, redefining how organizations think about cloud, latency, and execution. As new performance metrics like time-to-first-token gain importance, the discussion highlights how infrastructure decisions directly impact real-world AI outcomes.
Key Takeaways
🔹 AI deployment is shifting from centralized models to distributed inferencing environments
🔹 Time-to-first-token is emerging as a critical performance metric for AI workloads
🔹 Unified infrastructure is key to maintaining consistency across core and edge
🔹 Lenovo and Akamai are redefining what “cloud” means in the AI era
🔹 Edge AI is enabling faster, more secure, real-time decision-making
As AI scales, the ability to execute models consistently across distributed environments will define enterprise success.
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