The next AI challenge isn’t speed. It’s continuity.
Key-value (KV) caching has gone from an obscure inference optimization to one of the hottest topics in AI infrastructure. The basic idea is straightforward: as a large language model processes a prompt and generates a response, it performs calculations that would otherwise need to be repeated as the conversation grows. KV caching keeps some of those previously computed results available so the model can reuse them rather than doing the same work again. The result is less redundant computation and faster inference.
That solves an important problem inside the model. But AI workloads are beginning to create a similar problem at a much larger scale.
Agents do more than generate a response. They can gather information, call tools, query enterprise data, create files, make decisions and complete multiple steps toward a larger goal. Along the way, they accumulate valuable working state: context, memory, tool outputs, intermediate results, generated files, checkpoints...
Copyright of this story solely belongs to www.ciodive.com. To see the full text click HERE
