In AI, handling inference for large language models often means working without state continuity—the model processes each request in isolation, discarding computational data immediately afterward. Memory requirements increase linearly with the sequence length, creating bottlenecks for extensive contexts.

Agentic AI, unlike stateless models, involves systems capable of remembering and building upon previous interactions, leading to long-lasting inference contexts. This demands maintaining an extensive Key-Value (KV) cache, extending the lifespan of data states considerably.

This approach calls for a thorough re-evaluation of existing memory hierarchies, often pushing beyond their limits. GPUs’ high-bandwidth memory (HBM) is quick but limited, unsuitable for vast agentic contexts. System DRAM offers capacity but lacks the speed necessary for fluid AI processes.

Innovative solutions like Nvidia’s Inference Context Memory Storage and WEKA’s Augmented Memory Grid propose new near-compute memory tiers to bridge the speed-capacity gap. These solutions aim to reduce latency while ensuring memory resources are effectively distributed and utilized. Compute Express Link (CXL) interconnects further enhance flexibility by allowing memory pooling across various processors.

Accompanying these hardware adjustments, memory management software becomes crucial to optimize the handling of AI workloads by ensuring memory resources are allocated efficiently without disrupting processing tasks.

The rise of agentic AI shifts performance bottlenecks from computing power to memory management. The success of future AI systems will heavily depend on how well they manage data and memory within their architectures.