Amazon SageMaker Inference can now route requests with the same prompt beginning to the same model instance, allowing servers to reuse earlier computation. The feature targets applications where every request repeats a long block of instructions, documents or conversation history before adding a short user message.
Model servers store processed prompt prefixes in a key-value cache. In a conventional fleet, load balancing can scatter similar requests across machines, leaving each one to calculate the same prefix again. Prefix-aware routing recognizes the shared beginning and consistently directs it to an instance where that cache is already warm.
In Amazon’s benchmark with Llama 3.1 70B, the strategy raised cache hit rates from about 25 percent to more than 80 percent and reduced median time to first token by as much as 77 percent. The result applies to that test configuration rather than every model or workload; gains depend on how often prompts actually share substantial prefixes.
The change matters most for support bots, retrieval systems and other services with large repeated context. It does not make model generation itself faster after the first token. Instead, it reduces duplicated processing before generation starts, using routing rather than application changes to preserve useful cache locality across a scaled endpoint.