I did some model hacks, and got GLM5.2 from about 2.5 tok/s to >50 tok/s on my GH200 system.
Mirrored from r/LocalLLaMA for archival readability. Support the source by reading on the original site.
G'day.
This is part 3 on my Local LLM adventures. I have a crazy system hacked server-to-desktop system:
| Component | Spec |
|---|---|
| GPUs | 2x Hopper H100, 96 GB HBM3 each |
| CPUs | 2x Grace, 72 cores each |
| Host memory | 480 GB LPDDR5X per Grace, 960 GB total |
So I can run technically run GLM5.2. Except the naive settings were crap, like 2.5 tok/second on vLLM.
Messing with NUMA got me higher, but in the end I had to do some surgery, and I grafted the MTP head from the office zai's GLM-5.2-FP8 repo to the body of CyanKiwi's AWQ quant version.
You can do the same using these instructions. You have to pull all of CyanKiwi's weights, but only a few files from the zai repo; the script will merge the two. You also need to patch vLLM to deal with the changes.
This bumped the speed to a best case ~55 tok/sec at 4x concurrency and ~45 tok/sec for single inference, streaming from RAM to VRAM. Hope it comes in handy!
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