sglang-auto-benchmark

You can automate SGLang benchmark workflows by using a YAML-based configuration file to coordinate tiered server-flag sweeps, dataset preparation, and result export. This orchestrates the entire process end-to-end to produce reproducible results.

Yes, you can resume interrupted SGLang auto-benchmark runs. The workflow supports resumeable tiered searches, allowing you to continue benchmarking scenarios and server-flag sweeps without restarting the entire process from the beginning.

Canonical dataset preparation for SGLang benchmarking is handled automatically by the workflow. It manages dataset generation and canonicalization as part of its orchestration before executing the defined modeling and benchmarking scenarios.

Yes, the SGLang benchmarking tool is applicable to both local and remote deployments. It supports executing benchmark runs across different environments to compare configurations like QPS and latency tradeoffs on your own infrastructure.

The best way to compare QPS and latency tradeoffs is to use an automated benchmark workflow that manages model launch commands and result aggregation. This allows you to efficiently compare multiple model backends using defined server flags and datasets.

You configure server flags and speculative options for reproducible benchmarking using YAML-based configuration files. These files define the specific parameters for the runs, ensuring that the benchmarking scenarios produce consistent and comparable results.