EcoServe: Enabling Cost-effective LLM Serving with Proactive Intra- and Inter-Instance Orchestration
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Abstract (arXiv)
Existing LLM serving strategies can be categorized based on whether prefill and decode phases are disaggregated: non-disaggregated (NoDG) or fully disaggregated (FuDG). However, the NoDG strategy leads to strong prefill-decode interference and the FuDG strategy highly relies on high-performance interconnects, making them less cost-effective. We introduce EcoServe, a system that enables cost-effective LLM serving on clusters with commodity interconnects. EcoServe is built on the partially disaggregated (PaDG) strategy, applying temporal disaggregation and rolling activation for proactive intra- and inter-instance scheduling. It first disaggregates the prefill and decode phases along the time dimension within a single instance to mitigate inter-phase interference and enhance throughput. Next, it coordinates multiple instances and cyclically activates them to ensure the continuous availability of prefill processing, thereby improving latency. Thus, EcoServe's basic serving unit is the macro instance, within which multiple instances collaborate. It further integrates an adaptive scheduling algorithm to route requests in a macro instance and a mitosis scaling approach to enable fine-grained capacity scaling. Beyond delivering high goodput, EcoServe excels in load balancing, hardware cost, parallelism compatibility, and even engineering simplicity compared to existing solutions. When serving 30B- and 70B-scale models on a production-level cluster with 32 NVIDIA L20 GPUs using commodity Ethernet, EcoServe averagely improves goodput by 82.49%, 86.17%, 122.76%, and 126.96% over four representative NoDG and FuDG systems.
Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). First posted on arXiv 25 Apr 2025.
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Citing papers per year: 2025: 3; 2026: 1.
4 papers.
- 2025TokenScale: Timely and Accurate Autoscaling for Disaggregated LLM Serving with Token Velocitycited by 9
- 2025xLLM Technical Reportcited by 3
- 2025Revisiting Disaggregated Large Language Model Serving for Performance and Energy Implicationscited by 2
- 2026PipeMax: Enhancing Offline LLM Inference on Commodity GPU Serverscited by 0
- 2026PipeMax: Enhancing Offline LLM Inference on Commodity GPU Servers4 May 2026
- 2025TokenScale: Timely and Accurate Autoscaling for Disaggregated LLM Serving with Token Velocity3 Dec 2025
- 2025Revisiting Disaggregated Large Language Model Serving for Performance and Energy Implications14 Nov 2025
- 2025xLLM Technical Report16 Oct 2025
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- 2026DualScale: Energy-Efficient Disaggregated LLM Serving via Phase-Aware Placement and DVFS
- 2025DynaServe: Unified and Elastic Execution for Dynamic Disaggregated LLM Serving
- 2025Boosting LLM Serving through Spatial-Temporal GPU Resource Sharing
- 2025Sandwich: Joint Configuration Search and Hot-Switching for Efficient CPU LLM Serving
- 2026Towards Load-Aware Prefill Deflection for Disaggregated LLM Serving
- 2026Serving Hybrid LLM Loads with SLO Guarantees Using CPU-GPU Attention Piggybacking ↗
- 2026LAPS: A Length-Aware-Prefill LLM Serving System
- 2026Energy-Aware Scheduling for Serverless LLM Serving on Shared GPUs