generate-profile

Profile an SGLang server run by launching the server, polling for readiness, validating GSM8K accuracy, capturing a Chrome-compatible trace, and cleaning up server processes for reproducible GPU performance analysis.

Yes, Chrome trace format captures SGLang model serving latency and throughput diagnostics. The profiling process generates Perfetto-compatible traces that include server configuration metadata for detailed performance investigations.

Generating validated performance traces for multi-GPU SGLang deployments requires a working SGLang installation, CUDA GPU access, server health checks, an accuracy sanity test, configurable profiling steps, and cleanup of launched server processes.

Yes, SGLang profiling supports tensor-parallel configuration comparisons. Server orchestration launches with selected models, GPUs, ports, and tensor-parallel configurations to produce reproducible traces for diagnosing latency and throughput.

An accuracy sanity check using GSM8K runs before trace capture to validate that the SGLang server is fully operational and producing correct outputs, ensuring the collected performance metrics reflect a healthy deployment.

The best way to compare SGLang model-serving configurations is generating reproducible performance profiles that capture end-to-end traces across single- and multi-GPU deployments, suitable for diagnosing latency and throughput issues.