sglang-auto-benchmark

You can automate SGLang benchmarking by orchestrating dataset preparation, server flag sweeps, and result aggregation across tiered configurations. This coordinates end-to-end performance tuning workflows automatically, producing structured CSV outputs.

SLA and QPS benchmarking measures LLM serving performance against fixed service level agreements or queries per second targets. This Skill automates these benchmarks across tiered server flags and exports the structured results to CSV.

Yes, you can run optional second-stage speculative or EAGLE tuning after the initial benchmark. This allows iterative performance optimization by applying specialized tuning following the primary server flag sweeps.

Yes, SGLang auto benchmarking applies to iterative performance tuning across both local and remote GPU clusters. It includes resume capability to handle interruptions and robust logging to track benchmark progress.

You can prepare datasets through canonical dataset preparation, which includes auto-downloading ShareGPT and providing manual data conversion and validation. This ensures your data is correctly formatted before benchmarking begins.

Automation relies on running from canonical configs and producing structured outputs, which may limit highly custom benchmarking scripts. However, it supports manual data validation and optional health checks to maintain workflow robustness.