AWS ML BlogTuesday · October 20, 2026FREEPush-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM
The AWS Machine Learning Blog published a post titled "Push-based KV transfer to improve TTFT and TPOT for disaggregated inference in vLLM" on October 19, 2026. The title indicates the post concerns push-based key-value cache transfer in vLLM's disaggregated inference setup, framed around two serving metrics: time to first token (TTFT) and time per output token (TPOT). Disaggregated inference generally separates prompt processing from token generation across distinct workers, and the title places KV transfer between those stages at the center of the discussion. Beyond the headline, the supplied source text does not include the article body. What remains is the page's embedded CSS, consisting of WordPress preset color and gradient variable definitions, so no architecture description, implementation details, configuration flags, hardware setup, or measured results can be confirmed. The post does not, in the available text, state specific latency figures, throughput numbers, model names, or vLLM version numbers. Readers seeking the actual mechanism and any reported TTFT or TPOT improvements would need the full article, which is not present here.
The post signals AWS attention to KV transfer efficiency in disaggregated vLLM serving, a setup where prompt and generation stages exchange cache data.