Why LLM Inference Needs a New Kind of Router - Part 2

The text discusses the complexities and solutions involved in building a data layer for LLM routing, emphasizing the differences from traditional HTTP routing due to the stateful nature of inference backends. It highlights the need for a data structure that can handle fast concurrent reads, batched writes, and idempotent event processing to manage cached blocks across numerous pods with microsecond-level query latency. The solution involves using a HostBitmap to efficiently track which pods have cached particular blocks, employing sharding to minimize contention, and using Fibonacci hashing to distribute data evenly. The data layer processes block-level events such as registration and eviction, ensuring resilience to pod churn and maintaining accuracy for routing decisions. To efficiently determine which pods have cached blocks, a binary search over cumulative block hashes is used, allowing for rapid prefix matching. The system also incorporates a two-phase removal process to handle pod lifecycle changes without affecting query performance. This architecture enables fast cache-aware routing, essential for effective decision-making in inference orchestrators.

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