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Reading / 2026-05/2026-05-20t073125-how-to-cut-llm-inference-costs-with-kv-caching

How to Cut LLM Inference Costs with KV Caching

Argues that treating the KV cache as a persistent, shared data asset — injected from fast storage via RDMA rather than recomputed — can reduce prefill costs by up to 20x and dramatically improve token throughput in enterprise LLM deployments.

May 20, 2026 · tech · Robert Alvarez, Everpure Engineering

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Topics

  • llm-inference
  • ai-infrastructure
  • llm-engineering
  • production-systems
  • context-engineering

Cited by

  • AI infrastructure

    The systems, abstractions, and operational layers that make AI models usable at scale, from compute and caching to routing, governance, agent hosting, and credential management.

  • Context engineering

    Context engineering is the practice of deliberately constructing what an LLM receives in its context window — structuring, compressing, persisting, and retrieving information so agents produce reliable output across tasks and sessions.

  • LLM engineering

    LLM engineering spans the full stack of building with large language models: training, inference optimization, agent architecture, harness design, and the operational tradeoffs that determine whether model capability translates into reliable software.

  • LLM inference

    LLM inference covers how language models generate tokens from a prompt — spanning hardware constraints, serving architecture, caching strategies, quantization, routing, and cost — and has become its own engineering discipline as scale and cost pressures intensify.

  • Production systems

    The engineering decisions that determine how software behaves under real load, covering durability, observability, testing discipline, performance constraints, and the operational costs of failure.

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