https://www.infoq.com InfoQ Optimization Presentations feed https://www.infoq.com/presentations/llm-inference-kv-cache/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=Optimization-presentations <img src="https://res.infoq.com/presentations/llm-inference-kv-cache/en/mediumimage/medium-1792066105733.jpeg"/><p>Khawaja Shams shares a distributed systems perspective on scaling LLM inference stacks efficiently. He discusses practical strategies for optimizing GPU utilization, managing KV cache memory cliffs, leveraging tiered storage, reducing tail latency, and implementing disaggregated prefill and decode architectures to improve overall fleet performance, cost economics, and availability.</p> <i>By Khawaja Shams</i> QCon AI Boston 2026 Optimization Large language models Redis Caching Valkey Transcripts GPU Distributed Systems AI, ML & Data Engineering Development Architecture & Design presentation Tue, 27 Oct 2026 11:00:00 GMT https://www.infoq.com/presentations/llm-inference-kv-cache/?utm_campaign=infoq_content&utm_source=infoq&utm_medium=feed&utm_term=Optimization-presentations Khawaja Shams 2026-10-27T11:00:00Z /presentations/llm-inference-kv-cache/en