Medium LLM
8/10 signal
Caching LLM KV-Blocks in Aerospike: Notes from Building a Shared Prefix Cache
contextmemory
What happened
This article explores the technical implementation of a Large Scale Shared Prefix Cache for LLM KV-blocks using Aerospike. It details how to store and retrieve KV-caches across distributed systems to optimize inference latency and reduce compute costs for long-context and multi-turn interactions.
Why it matters
Distributed KV-cache management is essential for reducing the latency and cost of long-context, multi-turn agentic workflows.
The take
This is a highly valuable technical piece. As context windows grow and multi-turn agent interactions become standard, managing the KV-cache efficiently is a massive bottleneck. Offloading and sharing prefix caches via high-throughput databases like Aerospike is a highly practical optimization pattern for production-scale LLM platforms.
Do this
If you are building high-throughput, multi-turn LLM applications, investigate storing shared prefix KV-caches in a fast, distributed NoSQL database like Aerospike to optimize TTFT (Time to First Token).
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