阿里云 Tair 团队与 SGLang 社区共推 Agentic Cache 调度路线图
阿里云 Tair 团队在给 SGLang 做 KV 缓存调度，跨引擎共享缓存减少智能体场景的重复计算，做推理部署的可以看看。
阿里云 ApsaraDB 的 Tair KVCache 团队与 SGLang 社区共同主导 Agentic Cache Scheduling 路线图，将 HiCache 与并行计算、分布式推理集成。SGLang 最新博客介绍了 HiCache 如何配合 context parallelism、pipeline parallelism、speculative decoding 以及 prefill-decode 分离架构工作。方案还引入统一 KV 布局，让缓存在兼容的并行配置之间共享，减少智能体工作负载中的重复计算和响应延迟。
ApsaraDB's Tair KVCache team is co-leading SGLang's Agentic Cache Scheduling roadmap with the SGLang community. Together, we’re integrating HiCache with parallel computation and distributed inference, and developing cache sharing and scheduling across serving engines to reduce repeated work and response times for agentic workload.
The latest SGLang blog explains how HiCache works with context parallelism, pipeline parallelism, speculative decoding, and prefill-decode disaggregation. It also introduces a unified KV layout for sharing stored cache data across compatible parallel configurations.
Read the full blog 👇 HiCache Across Multi-Parallelism and Distributed Serving: https://t.co/xO5SLcz7w6 #AlibabaCloud #ApsaraDB #Tair #SGLang #Radixark
