Efficient LLM Serving on Commodity GPU Clusters with Data-Reduced Cross-Instance Orchestration

Existing LLM serving strategies can be categorized by whether prefill and decode phases are disaggregated: non-disaggregated (NoDG) or fully disaggregated (FuDG). However, they neither fit commodity GPU clusters, which remain widely deployed as mainstream AI infrastructure. NoDG suffers from severe prefill–decode interference, while FuDG depends heavily on high-performance interconnects that such clusters lack. We present EcoServe, an LLM serving system tailored to commodity GPU clusters. It enables a data-reduced collaboration among inference instances to mitigate prefill-decode interference, termed the partially disaggregated (PaDG) strategy. Particularly, within a single instance, PaDG disaggregates the prefill and decode phases along the time dimension to mitigate interference and enhance throughput. Next, it coordinates multiple instances and cyclically activates them to ensure the continuous availability of prefill processing, thereby rescuing latency. Thus, EcoServe’s basic serving unit is the macro instance, within which multiple instances collaborate. It further incorporates an adaptive scheduling algorithm to route requests in a macro instance and a mitosis scaling approach for fine-grained capacity adjustments in online scenario. On a 32-GPU NVIDIA L20 cluster over Ethernet, EcoServe improves goodput by 1.96×, 1.99×, 2.51×, and 2.40× when serving 30B- and 70B-scale LLMs, compared to four representative NoDG and FuDG systems, vLLM, Sarathi, DistServe, and MoonCake. EcoServe remains competitive even on an NVIDIA H100 cluster with NVLink and InfiniBand. Our code is released at https://github.com/MLSysU/EcoServe.