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sglang-auto-benchmark

Automate SGLang benchmark sweeps with dataset preparation and CSV export.

410|14|Updated Apr 8, 2026
Search Tags:#automation#ai#benchmark#reproducibility#datasets#sglang
One-click install
npx skills add https://github.com/OpenMOSS/MOSS-VL --skill sglang-auto-benchmark-openmoss
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: sglang-auto-benchmark
Source: https://github.com/OpenMOSS/MOSS-VL/tree/main/sglang/.claude/skills/sglang-auto-benchmark
Command: npx skills add https://github.com/OpenMOSS/MOSS-VL --skill sglang-auto-benchmark-openmoss

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Automates the end-to-end benchmarking workflow for SGLang by orchestrating tiered server flag sweeps, canonical dataset preparation, ShareGPT auto-download, and dataset validation, enabling repeatable AI performance exploration.

Core Features & Use Cases

  • Tiered server-flag sweeps to explore configuration space efficiently.
  • Canonical dataset preparation and conversion/validation for reproducible benchmarks.
  • ShareGPT auto-download and dataset handling with optional second-stage speculative/EAGLE tuning.
  • SLA or fixed-QPS benchmarking with CSV export for results and traceability.
  • Use Cases: teams seeking automated, AI-driven benchmark workflows rather than ad-hoc bench_serving commands.

Quick Start

Provide a minimal config and run the auto-benchmark to generate and export results to the specified output directory.

Frequently Asked Questions about sglang-auto-benchmark

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I automate SGLang benchmarking across different server configurations?▼

SGLang benchmarking is automated by a harness that orchestrates tiered server flag sweeps and dataset canonicalization. This replaces manual commands, coordinating configuration space exploration and ensuring reproducible AI performance tests across runs.

How do I prepare datasets for reproducible SGLang benchmarks?▼

Dataset preparation for SGLang benchmarks involves canonicalization, validation, and ShareGPT auto-download. The automation harness handles dataset conversion automatically, ensuring inputs are standardized for accurate and reproducible SLA or QPS performance testing.

Do I need a working SGLang environment to run automated benchmarks?▼

Yes, a working SGLang environment is required to run automated benchmarks. The harness orchestrates auto-benchmark runs within your existing setup, utilizing cookbook-style references to execute SLA and QPS tests without provisioning separate infrastructure.

Can I use speculative tuning with automated SGLang benchmarks?▼

Yes, automated SGLang benchmarks support optional second-stage speculative tuning, including EAGLE configurations. This allows you to explore speculative performance enhancements during SLA and fixed-QPS benchmarking runs within the coordinated flag sweep workflow.

What is the best way to export SGLang benchmark results for traceability?▼

Exporting SGLang benchmark results is best handled via automatic CSV export. The benchmarking harness generates and exports results to a specified output directory, capturing SLA and fixed-QPS metrics for experiment traceability and downstream performance analysis.

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