tile-ai / tile-ai/TileRT

Feature request: GLM-5.2 support / roadmap?

Open
#47 2 comments 0 reactions 0 assignees View on GitHub

Nobody has claimed this yet.

Dominant language
Python
Stars
1.8k
Forks
124
PR merge metrics
No merged PRs in 30d

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:

  1. Is GLM-5.2 support planned for TileRT?
  2. Would support require an official Z.ai W4A4/NVFP4 checkpoint, or could an FP8/NVFP4 conversion path be supported?
  3. If it is planned, is there any rough ETA?
  4. 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.

Contributor guide

No contributing guide indexed for this repository

First steps

  1. Read the whole issue, then the project's contributing guide.
  2. Comment on the issue to say you are picking it up — it saves two people doing the same work.
  3. Fork the repository and make your change on a branch.
  4. 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

Get new issues in your inbox

A short digest of beginner-friendly GitHub issues.