model-speed-benchmark

model-speed-benchmark is a Backend performance & caching skill for AI agents, published by sgl-project in the sglang-jax repository on GitHub. Skillspace rates model-speed-benchmark B (81/100) and it passed the safety scan; the rating is based on static analysis only.

Use when running throughput/latency (serving speed) benchmarks against an already-running model server. Uses sgl_jax.bench_serving to sweep batch-size/concurrency and measure throughput and latency, producing summary.csv and per-point bs_<N>/result.jsonl. Speed only — accuracy/eval belongs to model-accuracy-benchmark. Triggers on throughput, latency, bench_serving, batch-size sweep, tok/s, TTFT, TPOT — not accuracy/eval.

Grade

B

Quality

81 / 100

Safety

Passed

GitHub stars

311

Unproven: rated by static analysis only, not yet compared head-to-head.

Not launch-quality. It is graded B with a clean safety verdict, but our quality filters exclude it: it looks written only for the repository it lives in. So it is not in our launch-quality set, the skills our MCP server searches and recommends. How launch-quality is decided

Our two independent classifiers differ on this skill's category, so treat the placement as provisional.

This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.

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