[Tiny] Improve docs

[mmangkad]

Motivation

Modifications

Accuracy Tests

Benchmarking and Profiling

Checklist

• Format your code according to the Format code with pre-commit.
• Add unit tests according to the Run and add unit tests.
• Update documentation according to Write documentations.
• Provide accuracy and speed benchmark results according to Test the accuracy and Benchmark the speed.
• Follow the SGLang code style guidance.

Review Process

1. Ping Merge Oncalls to start the PR flow. See the PR Merge Process.
2. Get approvals from CODEOWNERS and other reviewers.
3. Trigger CI tests with comments or contact authorized users to do so.
   • /tag-run-ci-label, /rerun-failed-ci, /tag-and-rerun-ci
4. After green CI and required approvals, ask Merge Oncalls to merge.

[gemini-code-assist]

Summary of Changes

Hello @mmangkad, I'm Gemini Code Assist¹! I'm currently reviewing this pull request and will post my feedback shortly. In the meantime, here's a summary to help you and other reviewers quickly get up to speed!

This pull request focuses on refining the installation documentation, particularly for users working with CUDA 13 and specific hardware like GB300. The changes aim to provide clearer, more comprehensive instructions for installing sgl_kernel wheels and using Docker images, thereby improving the overall user experience during setup.

Highlights

• Documentation Update for CUDA 13: The installation guide has been updated to explicitly mention GB300 alongside B300 for CUDA 13 environments, ensuring users on these platforms receive accurate guidance.
• Improved sgl_kernel Installation Clarity: Instructions for manually installing sgl_kernel wheels have been enhanced by adding a tip on how to find the required version using uv pip show sgl_kernel and by reformatting the example commands for better readability.
• Markdown Link Conversion: A raw URL to the sgl-project whl releases has been converted into a proper markdown link, improving the visual presentation and user experience.

🧠 New Feature in Public Preview: You can now enable Memory to help Gemini Code Assist learn from your team's feedback. This makes future code reviews more consistent and personalized to your project's style. Click here to enable Memory in your admin console.

Using Gemini Code Assist

The full guide for Gemini Code Assist can be found on our documentation page, here are some quick tips.

Invoking Gemini

You can request assistance from Gemini at any point by creating a comment using either /gemini <command> or @gemini-code-assist <command>. Below is a summary of the supported commands on the current page.

Feature	Command	Description
Code Review	/gemini review	Performs a code review for the current pull request in its current state.
Pull Request Summary	/gemini summary	Provides a summary of the current pull request in its current state.
Comment	@gemini-code-assist	Responds in comments when explicitly tagged, both in pull request comments and review comments.
Help	/gemini help	Displays a list of available commands.

Customization

To customize Gemini Code Assist for GitHub experience, repository maintainers can create a configuration file and/or provide a custom code review style guide (such as PEP-8 for Python) by creating and adding files to a .gemini/ folder in the base of the repository. Detailed instructions can be found here.

Limitations & Feedback

Gemini Code Assist may make mistakes. Please leave feedback on any instances where its feedback is incorrect or counter productive. You can react with 👍 and 👎 on @gemini-code-assist comments. If you're interested in giving your feedback about your experience with Gemini Code Assist for Github and other Google products, sign up here.

You can also get AI-powered code generation, chat, as well as code reviews directly in the IDE at no cost with the Gemini Code Assist IDE Extension.

Footnotes

1. Review the Privacy Notices, Generative AI Prohibited Use Policy, Terms of Service, and learn how to configure Gemini Code Assist in GitHub here. Gemini can make mistakes, so double check it and use code with caution. ↩

[Copilot]

Pull request overview

This PR improves the installation documentation for SGLang by adding GB300 hardware references and enhancing clarity around CUDA 13 installation instructions.

Changes:

• Added GB300 hardware references alongside B300 for consistency
• Converted plain URL to markdown link for better formatting
• Added helpful command to find the sgl_kernel version
• Improved code example formatting with inline comments

---

💡 Add Copilot custom instructions for smarter, more guided reviews. Learn how to get started.

[gemini-code-assist]

Code Review

This pull request improves the installation documentation by adding information for GB300, clarifying instructions for CUDA 13, and improving the formatting of code examples. The changes are beneficial for users. I have provided one suggestion to further enhance the clarity of the installation commands for different machine architectures.