Managed Tiered KV Cache and Intelligent Routing for Amazon SageMaker HyperPod

Modern AI applications demand fast, cost-effective responses from large language models, especially when handling long documents or extended conversations. However, LLM inference can become prohibitively slow and expensive as context length increases, with latency growing exponentially and costs mounting with each interaction.

LLM inference requires recalculating attention mechanisms for the previous tokens when generating each new token. This creates significant computational overhead and high latency for long sequences. Key-value (KV) caching addresses this bottleneck by storing and reusing key-value vectors from previous computations, reducing inference latency and time-to-first-token (TTFT). Intelligent routing in LLMs is a technique that sends requests with shared prompts to the same inference instance to maximize the efficiency of the KV cache. It routes a new request to an instance that has already processed the same prefix, allowing it to reuse the cached KV data to accelerate processing and reduce latency. However, customers have told us that ...