Really like the framing of KV cache as the variable cost of intelligence.
One implication I think becomes especially important at scale is that KV cache stops being just a GPU-memory optimization and starts becoming a distributed-systems problem. Once prefixes are reusable, the questions become: where does that cached state live, how do you route requests toward it, when is moving the cache cheaper than recomputing it, and how do you balance locality against GPU utilization?
That makes “memory-smart infrastructure” much broader than simply fitting more KV into HBM, it starts influencing scheduling, routing, placement, and even model architecture.
I recently explored that side of the problem in more detail here:
the KV cache as the real variable cost is sharp, this matches the AI infra efficiency we wrote about recently
Really like the framing of KV cache as the variable cost of intelligence.
One implication I think becomes especially important at scale is that KV cache stops being just a GPU-memory optimization and starts becoming a distributed-systems problem. Once prefixes are reusable, the questions become: where does that cached state live, how do you route requests toward it, when is moving the cache cheaper than recomputing it, and how do you balance locality against GPU utilization?
That makes “memory-smart infrastructure” much broader than simply fitting more KV into HBM, it starts influencing scheduling, routing, placement, and even model architecture.
I recently explored that side of the problem in more detail here:
https://behindintelligentsystems.substack.com/p/the-memory-that-makes-ai-fast