ServerBench benchmarks agent-designed LLM serving systems
Original title: ServerBench: How can your coding agents design specialized LLM serving systems?
Claude Sonnet 5.5 achieves 4.87 times vLLM reference on Gemma E4B via agentic design.
LLM coding agents offer potential to design optimal serving systems, yet lack systematic benchmarks. ServerBench evaluates agents on dollars per dollar objective under session dependencies and latency constraints. Stateful replay and independent verification are used across 11 agents and three deployments, with 22 of 33 runs yielding verified systems.
- Claude Sonnet 5.5 reaches 2.97 dollars per dollar on Gemma E4B
- 22 of 33 runs produce verified systems
- 13 exceed strongest baseline performance
- Products
- Gemma E4B
- AI models
- Claude Sonnet 5.5
From the original article
1 min read at arxiv.orgLarge language model (LLM) coding agents develop software through experimentation, offering an opportunity to design the serving systems supporting their own operation. Effective serving requires coordinating implementation and operating decisions under session dependencies, latency constraints, and correctness requirements.
By Xinrui Zhong · arxiv.org
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