AI代理自动生成定制化大模型服务系统，性能不输传统方案。
VibeServe: Can AI Agents Build Bespoke LLM Serving Systems?
- 用多智能体循环自动设计专属服务架构，端到端生成系统。
- 标准场景下性能媲美vLLM，非标场景下提升显著。
- 适合需要定制化部署的开发者与研究者。
长期以来，大模型服务系统如同其他关键基础设施，采用单一通用架构，经多年人工调优以支持各类模型和负载。本文提出VibeServe，首个实现端到端自动生成专用服务系统的智能体循环。其外层负责规划与追踪系统设计搜索，内层执行候选方案、验证正确性并测量目标基准表现。在标准部署场景中，VibeServe性能与vLLM相当，证明生成时定制化无需牺牲性能；更有趣的是，在六种非标准场景（涉及非标模型架构、负载知识、硬件优化）中，其显著优于现有系统，捕捉了通用系统忽略的优化机会。结果表明，基础设施软件可探索生成时定制而非运行时通用的新设计空间。代码已开源：https://github.com/uw-syfi/vibe-serve。
原文摘要 · Abstract (English)
For years, we have built LLM serving systems like any other critical infrastructure: a single general-purpose stack, hand-tuned over many engineer-years, meant to support every model and workload. In this paper, we take the opposite bet: a multi-agent loop that automatically synthesizes 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 the standard deployment setting, where existing stacks are highly optimized, VibeServe remains competitive with vLLM, showing that generation-time specialization need not come at the cost of performance. More interestingly, in non-standard scenarios, VibeServe outperforms existing systems by exploiting opportunities that generic systems miss in six scenarios involving non-standard model architectures, workload knowledge, and hardware-specific optimizations. Together, these results suggest a different point in the design space for infrastructure software: generation-time specialization rather than runtime generality. Code is available at https://github.com/uw-syfi/vibe-serve.
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