KV Cache Is Not About Speed: It's About Surviving Inference Costs

Most teams think KV cache is about making inference faster. That's true, but it's not the point. KV cache is really about controlling memory, reducing recomputation, and keeping costs from spiraling as usage grows. As models get deployed at scale, the real bottleneck is no longer raw compute. It's memory, bandwidth, and power. This session takes a step back to explain what KV cache does at a system level, why it matters for real workloads, and how approaches like vLLM, LMCache, and SGLang change how we think about scaling inference. We'll also connect this to a problem many teams are already seeing: confidently incorrect answers in agent systems. When cache behavior, context reuse, and routing aren't designed well, systems don't just get slower or more expensive. They get inconsistent. And that shows up as wrong answers with high confidence. This session will walk through these trade-offs using live demos, showing how different KV cache strategies impact cost, latency, and output quality in real time.