AgentServe: Algorithm-System Co-Design for Efficient Agentic AI Serving on a Consumer-Grade GPU

A research team from academia has published a paper on arXiv detailing AgentServe, a new inference serving system specifically designed for the unique demands of AI agents running on consumer-grade GPUs. Unlike traditional chatbot serving, agentic workloads involve complex reasoning-action loops where models interleave computation with external tool calls, creating a mix of long 'prefill' phases (processing system prompts) and short, latency-critical 'decode' phases. When multiple agents run concurrently on a single GPU, these heterogeneous requests contend for resources, causing head-of-line blocking that destroys interactive performance. AgentServe tackles this by analyzing agent execution patterns and separating them into cold prefills, resume prefills (which append tool outputs), and decodes.

AgentServe's core innovation is a co-designed algorithm and system architecture that isolates prefills from decodes to prevent blocking. It applies dynamic budgeting to manage resume prefills and allocates GPU resources through pre-established CUDA Green Context slots with adaptive control. This approach ensures stable token emission and low latency even as multiple agent requests arrive. The evaluation shows dramatic improvements over existing serving systems, with up to 2.8x better Time to First Token (TTFT) and 2.7x better Throughput per Operating Time (TPOT). This performance makes it feasible to run sophisticated, multi-step AI agents locally on hardware like an RTX 4090, addressing key privacy, compliance, and cost constraints that are pushing development away from cloud APIs.