It might make sense to have big model, slow local compute to calculate answers to hard private ongoing problems, like balancing a portfolio. Let it sit in the closet and message you with orders to approve.
I'm not fully understanding this business of MoE so please forgive me if this is a dumb question, but would it be possible to use MPI with a small cluster to distribute the load?
My main question is whether when put into practical use, this can be measured in tokens/second, or more like 1 token per minute... I have seen locally hosted LLM that are as slow as 1 tok/second still be very useful if you give it a project to do something overnight and metaphorically walk away from it, check back with what it has done in 6 or 8 hours.
0.05 to 0.1 tok/s on the other hand, as reported in the URL for the lowest class of hardware, isn't really usable for much.
edit: I think this is a fantastic project in general concept, and look forward to seeing more efforts towards the general idea of being able to run a 350B to 900B size model locally, even if as slow as 1 tok/s, on hardware that ordinary people can afford. Anything along the general concept of "we have fast read NVME SSD storage, we have a big ass model on local disk, we'll read it at 11GB/tok as we need it, not try to load the whole thing".
In the readme you can see benchmark which everyone with different hardware is running Colibrì, and I have to say I've seen great times! I'm always doing more to improve!
I have a 16-core system with 256GB RAM here I could try it with but regretfully it's so old the CPUs aren't AVX2 capable. Otherwise it makes a fairly good llama-server test system for CPU only stuff. Oh well. Time to upgrade (painful to the wallet these days).
Pretty cool! I've also been playing around with GLM 5.2 this week and was equally impressed. At work we're running it locally on some crazy expensive hardware as a test before starting another project so it's great to see people taking this massive FOSS model release and running it on an average machine, even if it's not terribly practical at this point.
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.
What causes problems is the rewriting in this case are only read while writing is the cache! However, I'm working to improve more and more and make some parts lighter!
0.05 to 0.1 tok/s on the other hand, as reported in the URL for the lowest class of hardware, isn't really usable for much.
edit: I think this is a fantastic project in general concept, and look forward to seeing more efforts towards the general idea of being able to run a 350B to 900B size model locally, even if as slow as 1 tok/s, on hardware that ordinary people can afford. Anything along the general concept of "we have fast read NVME SSD storage, we have a big ass model on local disk, we'll read it at 11GB/tok as we need it, not try to load the whole thing".
Nice work!
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.
https://github.com/JustVugg/colibri#ssd-wear-warning
Amazing job!