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Comment by lacoolj | Hacker News Reader

you can run the 120b model on an 8GB GPU? or are you running this on CPU with the 64GB RAM?

I'm about to try this out lol

The 20b model is not great, so I'm hoping 120b is the golden ticket.

With MoE models like gpt-oss, you can run some layers on the CPU (and some on GPU): https://github.com/ggml-org/llama.cpp/discussions/15396

Mentions 120b is runnable on 8GB VRAM too: "Note that even with just 8GB of VRAM, we can adjust the CPU layers so that we can run the large 120B model too"

I have in many cases had better results with the 20b model, over the 120b model. Mostly because it is faster and I can iterate prompts quicker to choerce it to follow instructions.

> had better results with the 20b model, over the 120b model

The difference of quality and accuracy of the responses between the two is vastly different though, if tok/s isn't your biggest priority, especially when using reasoning_effort "high". 20B works great for small-ish text summarization and title generation, but for even moderately difficult programming tasks, 20B fails repeatedly while 120B gets it right on the first try.

But the 120b model has just as bad if not worse formatting issues, compared to the 20b one. For simple refactorings, or chatting about possible solutions i actually feel teh 20b halucinates less than the 120b, even if it is less competent. Migth also be because of 120b not liking being in q8, or not being properly deployed.

> But the 120b model has just as bad if not worse formatting issues, compared to the 20b one

What runtime/tools are you using? Haven't been my experience at all, but I've also mostly used it via llama.cpp and my own "coding agent". It was slightly tricky to get the Harmony parsing in place and working correct, but once that's in place, I haven't seen any formatting issues at all?

The 20B is definitely worse than 120B for me in every case and scenario, but it is a lot faster. Are you running the "native" MXFP4 weights or something else? That would have a drastic impact on the quality of responses you get.

Edit:

> Migth also be because of 120b not liking being in q8

Yeah, that's definitely the issue, I wouldn't use either without letting them be MXFP4.

Everything I run, even the small models, some amount goes to the GPU and the rest to RAM.

Hmmm...now that you say that, it might have been the 20b model.

And like a dumbass I accidentally deleted the directory and didn't have a back up or under version control.

Either way, I do know for a fact that the gpt-oss-XXb model beat chatgpt by 1 answer and it was 46/50 at 6 minutes and 47/50 at 1+ hour. I remember because I was blown away that I could get that type of result running locally and I had texted a friend about it.

I was really impressed but disappointed at the huge disparity between time the two.

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