VibeServe: Can AI Agents Build Bespoke LLM Serving Systems?

Abstract

For years, we have built LLM serving systems like any other critical infrastructure, with a single general-purpose stack hand-tuned over many engineer-years to support every model and workload. In this paper, we take the opposite bet and let coding agents automatically synthesize bespoke serving systems for different usage scenarios. We propose VibeServe, the first agentic loop that generates entire LLM serving stacks end-to-end. VibeServe uses an outer loop to plan and track the search over system designs, and an inner loop to implement candidates, check correctness, and measure performance on the target benchmark. In standard serving settings, VibeServe matches vLLM and SGLang on Llama-3.1-8B, and delivers 2.3x the goodput of a tuned SGLang for a 397B MoE model on four AMD MI300As. In five non-standard scenarios, VibeServe outperforms existing systems by exploiting opportunities that generic systems miss, spanning non-standard model architectures, workload knowledge, and hardware-specific optimizations. Together, these results argue that infrastructure software should favor generation-time specialization over runtime generality. Code is available at https://github.com/uw-syfi/vibesys.