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RESEARCH · RESEARCH · #2646

Momento CEO Khawaja Shams presents 'Serving LLMs at Scale: The Hidden KV Cache Advantage' at QCon AI

Khawaja Shams (Co‑Founder & CEO of Momento) gave a QCon AI presentation describing distributed-systems approaches to scale LLM inference efficiently, focusing on GPU utilization, KV cache memory cliffs, tiered storage, tail-latency reduction, and disaggregated prefill/decode architectures. He framed these techniques as ways to improve fleet performance, cost economics, and availability for production LLM deployments.

KEY POINTS

  1. Khawaja Shams (Co‑Founder & CEO of Momento) gave a QCon AI presentation describing distributed-systems approaches to scale LLM inference efficiently, focusing on GPU utilization, KV cache memory cliffs, tiered storage, tail-latency reduction, and disaggregated prefill/decode architectures.
  2. He framed these techniques as ways to improve fleet performance, cost economics, and availability for production LLM deployments.
  3. Practical KV cache and disaggregated inference strategies can materially raise GPU utilization and reduce cost and tail latency for production LLM services.

WHY IT MATTERS

Practical KV cache and disaggregated inference strategies can materially raise GPU utilization and reduce cost and tail latency for production LLM services.

SOURCES & TIMELINE

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INFOQ AI, ML & DATA ENGINEERING INDEPENDENT COVERAGE
Presentation: Serving LLMs at Scale: The Hidden KV Cache Advantage

InfoQ Homepage Presentations Serving LLMs at Scale: The Hidden KV Cache Advantage 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…

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