Show HN: Getting GLM 5.2 running on my slow computer

I think if you had something like a theoretical used/refurb 2U rackmount server with two older multi core CPUs, 768GB of RAM, you would see faster performance loading a Q6 or Q8 GGUF of GLM5.2 into a freshly-compiled latest copy of llama-server, with the "no-mmap" option turned on to intentionally load the whole thing into RAM at the time the llama-server daemon launches.

If you want a CPU-only machine with 512GB to 1024GB of RAM, despite extreme cost rises, there are still some great options out there from companies selling ex-lease stuff that's 3, 4, 5 years old. It'll be loud as hell under full CPU load when running inference, so if you plan to use it at home, put it in your garage or basement or laundry room or somewhere similar on the far end of a network cable.

The software that OP has published appears to be specifically designed to hold only the active parameters in RAM (<100GB) and read content off local NVME SSD as needed on the fly. All that NVME SSD read wouldn't be necessary if you can hold the model in RAM, even in the absence of any GPUs.

I was actually just working on the same thing as this, but I went down the route of mmapping the entire model into memory to avoid the extra ram usage. I also had Claude implement Medusa[1] on the model to try and avoid loading an additional model into memory but still get the benefits of MTP. Currently at a stop light so I can't list everything and I didn't get to read your full post either yet.

To expand since I just got home, I'm making all of my modifications to llama.cpp, the goal was to eventually put this on a SBC of some kind with an nvme to handle the mmapped files. I think the theoretical limit of my current setup is about 1.8 tok/s based on prior testing but that is also with the additional medusa heads not fully trained (I honestly don't know if the counting it's generated tokens or not.)

In the end it seems like the idea we had is similar, I just don't know how to write an llm parser/runner from scratch yet and instead of specifying what needed to stay in memory I just let the linux kernel handle it.

Oh last note, I also capped llama.cpp usage to 16GB of my 32GB, so it might be possible to get it down even lower.

[1] https://arxiv.org/abs/2401.10774

Is it possible to run this into an agent? pi, claude code, etc..? I've only tried it with LM studio, but i'm guessing this is a bit different

if you like, colibrì always needs to improve so if you have ideas or anything else you are welcome for pull request issues and also benchmarks!