sgl-project / sgl-project/SpecForge
[Question]What factors contribute to Eagle3's reduced acceleration performance on VLM architectures compared to traditional LLMs?
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Description
First, sincere thanks to the SGLang community for enabling the rapid deployment of Eagle3 on state-of-the-art VLMs such as Qwen2.5-VL.
In my preliminary tests conducted under identical settings, however, the speed-up brought by Eagle3 is noticeably smaller than that observed on language models (more detailed plz see https://github.com/sgl-project/sglang/pull/8801#issuecomment-3615087060).
Could you kindly share any insights into what might be causing this gap? Any guidance would be highly appreciated ：）
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First steps
- Read the whole issue, then the project's contributing guide.
- Comment on the issue to say you are picking it up — it saves two people doing the same work.
- Fork the repository and make your change on a branch.
- Open a pull request that references the issue number.
Research direction
Start with the linked SGLang pull-request comment and compare the reported Eagle3 results for Qwen2.5-VL with the identical-settings language-model results. The issue is done when the factors behind the smaller VLM speed-up are identified and documented clearly enough to guide further investigation.
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Assessment
- Tech stack
- python, pytorch
- Domain
- ai, machine-learning, performance
- Issue type
- Documentation
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100