[Docs] Add Encoder (ViT) CUDA Graphs section to CUDA Graphs design doc

[b-mu]

Summary

• Add a new "Encoder (ViT) CUDA Graphs" section to docs/design/cuda_graphs.md, documenting the encoder CUDA graph feature from [Feature] ViT Full CUDA Graph #35963
• Covers motivation, budget-based capture/replay design, greedy bin-packing algorithm, data-parallel support,
  SupportsEncoderCudaGraph protocol, configuration options, and usage examples (CLI + Python)
• Add table of contents entry linking to the new section

cc @maxyanghu @wangshangsam @Isotr0py @ywang96

Test plan

• Built docs locally with mkdocs serve and verified the new section renders correctly (headings, code blocks,
  admonitions, table of contents links)
• No existing content modified other than adding the table of contents entry

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Essential Elements of an Effective PR Description Checklist

• The purpose of the PR, such as "Fix some issue (link existing issues this PR will resolve)".
• The test plan, such as providing test command.
• The test results, such as pasting the results comparison before and after, or e2e results
• (Optional) The necessary documentation update, such as updating supported_models.md and examples for a new model.
• (Optional) Release notes update. If your change is user facing, please update the release notes draft in the Google Doc.

[mergify]

Documentation preview: https://vllm--37914.org.readthedocs.build/en/37914/

[gemini-code-assist]

Code Review

This pull request adds comprehensive documentation for Encoder (ViT) CUDA Graphs to the docs/design/cuda_graphs.md file. The new section details the motivation for using CUDA Graphs for vision encoders, their budget-based capture and replay design, model integration via the SupportsEncoderCudaGraph protocol, configuration options, and usage examples. There are no review comments to address.

[gemini-code-assist]

Code Review

This pull request adds a new section to the cuda_graphs.md documentation, detailing the "Encoder (ViT) CUDA Graphs" feature, including its motivation, design, model integration, configuration, and usage. The reviewer suggests improving the "About the Performance" section by linking to specific performance examples or benchmarks, or by adjusting the section title.

[Isotr0py]

I think we can separate ViT Cuda graph doc into single ones like docs/design/torch_compile_multimodal.md

[b-mu]

I have moved it to a seperate file docs/design/cuda_graphs_multimodal.md

[shen-shanshan]

@b-mu @wangshangsam

Hello, I also want to help improve this feature (ViT CG) in vLLM and maybe we can propose a RFC to track all the related PRs. How do you think?

I suppose there are some other things we can do in the future:

1. Support video inference for Qwen3-VL ViT CG (I'm currently working on this).
2. Support more models, e.g., Qwen3.5, GLM-V, Kimi K2.5.
3. Make benchmark comparisons for more scenarios and offer tuning guide for users to better use feature, since it's not always beneficial to performance. [Feature] ViT Full CUDA Graph #35963 (comment)

[wangshangsam]

@shen-shanshan

maybe we can propose a RFC to track all the related PRs. How do you think?

Personally I think GitHub Projects is a better tool for tracking issues and PRs. Currently, all the features that we (i.e., NV) developed for the MLPerf Inference VLM benchmark (some of them) are (and some of them planed to be; I was just asking for edit permission from @ywang96 ) tracked by the Qwen3.5 project board (which is maintained by @ywang96 @vadiklyutiy ), in addition to the NVIDIA project board (which I tried to maintain from time to time but for the most part it's running by itself through workflows).

The thing with either a project board or a RFC (i.e., tracking issue) is that it's only useful when someone actively maintains it, and I'm not sure if the scope of ViT-cudagraph-related features are big enough to justify standalone project management efforts (in the greater context of how busy everyone is). If you would like to track them, I would propose to track them in the same Qwen3.5 board (I know that the name is less than ideal, but at least that project board is being actively maintained and looked at in terms of multimodality features). Otherwise, my leadership philosophy is about always minimizing management overhead and empowering others instead of being gatekeepers (not that this matters but I'm just trying to explain my thought process), so just submitting the PR(s) that would support video inference for Qwen3-VL ViT CG and (if needed) discussing through slack might be good enough, and we'd be happy to help and provide feedback on concrete technical problems.

[wangshangsam]

With that being said, @shen-shanshan if you would like to commit time and maintain a tracking issue for ViT-cudagraph-related features, please feel very welcome to go ahead :)

[shen-shanshan]

With that being said, @shen-shanshan if you would like to commit time and maintain a tracking issue for ViT-cudagraph-related features, please feel very welcome to go ahead :)

Thanks for your patient explanation! Currently, I'm working on vLLM multi-modal related things full-time and I'm sure I can commit time to maintain a tracker issue (only focus on CG for ViT).