Amazon SageMaker Adds Prefix-Aware Routing to Cut LLM Latency
Amazon SageMaker Inference now supports prefix‑aware routing, a strategy that sends requests with identical prompt beginnings to the same instance. By keeping the key‑value cache warm, the feature cuts the time‑to‑first‑token (TTFT) for Llama 3.1 70B by up to 77 % and boosts throughput by roughly 16 %. The technique also raises cache hit rates from about 25 % to over 80 %, with only a 1.3–1.9 ms…
Key points
- Prefix‑aware routing sends identical prompt prefixes to the same instance, boosting cache reuse.
- Benchmarks on Llama 3.1 70B show up to 77 % TTFT reduction and 16 % throughput increase.
- The feature adds only 1.3–1.9 ms per request and works with vLLM/TensorRT‑LLM without container changes.
The new routing mode is ideal for workloads that share long prefixes, such as retrieval‑augmented generation, multi‑turn conversations, templated bots, and code‑completion tools. It works with popular serving frameworks like vLLM and TensorRT‑LLM, and can be enabled via the endpoint configuration without modifying the model container.
Users can tune the strategy with PrefixLength and ConcurrencyThreshold parameters, and the system automatically balances traffic and protects against overload. The feature is available today on SageMaker real‑time inference endpoints and requires only an updated SDK or CLI.
The story so far
10 episodes →- Amazon SageMaker Adds Prefix-Aware Routing to Cut LLM Latencythis story
Reduce LLM latency with prefix-aware routing on Amazon SageMaker Inference
AWS Machine Learning Blog · 10 September 2026
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