Feature request: GLM-5.2 support / roadmap?
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- Dominant language
- Python
- Stars
- 1.8k
- Forks
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Description
Hi TileRT team,
Thanks for releasing TileRT. We are evaluating low-latency GLM-5.2 serving on 8xB200 and wanted to ask whether GLM-5.2 support is on the roadmap, and if there is any tentative ETA or blocker list.
Context:
- I saw the helpful response in #41 that GLM-5.1 NVFP4 support is not straightforward because unlocking the B200 NVFP4 tensor core path requires W4A4 / model-side support.
- GLM-5.2 appears to differ from GLM-5.1 architecturally, including shared DSA-related weights, and public deployments have mentioned FP8 weights plus NVFP4 quantization for Blackwell.
- We are mainly trying to understand whether TileRT could support GLM-5.2 decode on Blackwell, even if the first supported path is FP8 or requires a specific calibrated NVFP4/W4A4 checkpoint.
A few questions:
- Is GLM-5.2 support planned for TileRT?
- Would support require an official Z.ai W4A4/NVFP4 checkpoint, or could an FP8/NVFP4 conversion path be supported?
- If it is planned, is there any rough ETA?
- If community contributions are welcome, which pieces would be most useful: weight conversion, shared DSA handling, MoE routing, MTP/speculation support, benchmark harnesses, or something else?
We can help test on 8xB200 and share logs/benchmarks if useful.
Thanks again for the work on TileRT.
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- 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 by reviewing issue #41 and the current GLM-5.1, Blackwell, and quantization support described in the repository. Determine whether GLM-5.2 support would require shared DSA handling, MoE routing, MTP/speculation, weight conversion, or benchmark work; done would require an agreed implementation scope and validation on 8xB200.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- backend, machine-learning
- Issue type
- Feature
- Difficulty
- 5/5
- Estimated time
- Over a week
- Activity status
- Quiet
- Clarity
- Needs clarification
- Newbie friendliness
- 25/100