ToolsSeptember 10, 2026via AWS Machine Learning Blog
Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference
Why it matters
A practical inference optimization that practitioners deploying LLMs on AWS can adopt immediately to reduce latency in production — especially valuable for multi-user or high-volume inference workloads where cache efficiency directly impacts user experience.
Key signals
- Amazon SageMaker Inference feature: prefix-aware routing
- P50 time-to-first-token reduced up to 77% on Llama 3.1 70B
- KV cache hit rates improved from ~25% to over 80%
- Routes requests with same prompt prefix to same instance for cache reuse
- Prefix-aware routing routes requests with shared prompt prefixes to same instance
- P50 time-to-first-token reduced up to 77% on Llama 3.1 70B benchmark
- KV cache hit rates improved from ~25% to >80%
- Feature now available on Amazon SageMaker Inference
- Targets inference latency and cost efficiency for production deployments
The hook
77% faster first-token latency. Amazon SageMaker's new prefix-aware routing keeps KV cache warm by routing similar requests to the same instance.
Amazon SageMaker Inference now offers prefix-aware routing, a routing strategy that sends requests sharing the same prompt prefix to the same instance so the KV cache stays warm. In benchmarks on Llama 3.1 70B, it reduced P50 time-to-first-token by up to 77% and raised KV cache hit rates from about …