The Agentic AI Revolution & The Future of Inference

In large-scale LLM serving, recomputing inference sessions is a wasteful misuse of valuable GPU resources. This challenge is becoming critical with the rise of Agentic AI. As these autonomous systems devise their own strategies, they create long-running sessions and massive context windows that on-GPU KV caches alone cannot handle.

For the developers and architects building this future, the key to performance lies in decoupling context data from the GPU. Join this session to learn how to build a resilient, scalable, and cost-effective platform ready for the agentic era.

In this webinar, you will learn:

• The unique computational demands of Agentic AI move from simple instruction-following to long-term, stateful execution

• Why the quadratic cost (O(n2)) of self-attention makes on-GPU memory economically unviable for the long contexts generated by agentic workflows

• How a global, shared KV Cache provides the architectural foundation for agents to efficiently "re-think" by instantly reusing pre-computed information, slashing latency, and overcoming local memory bottlenecks