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GPT-OSS-120B runs on just 8GB VRAM & 64GB+ system RAM

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Re: GPT-OSS-120B runs on just 8GB VRAM & 64GB+ system RAM

#4
I have a 5950x with 128 gb ram and a 12 gb 3060 gpu. The speed of generating tokens is excellent, the killer is that when the context grows even a little processing of it is super slow. Hopefully someone smart will optimize this, but as it is now I keep using other models like qwen, mistral and gemma.

Re: GPT-OSS-120B runs on just 8GB VRAM & 64GB+ system RAM

#5
post #4

I have a 5950x with 128 gb ram and a 12 gb 3060 gpu. The speed of generating tokens is excellent, the killer is that when the context grows even a little processing of it is super slow. Hopefully someone smart will optimize this, but as it is now I keep using other models like qwen, mistral and gemma.

I would so appreciate concrete data instead of subjectivities like "excellent" and "super slow".

How many tokens is excellent? How many is super slow? How many is non-filled context?

Re: GPT-OSS-120B runs on just 8GB VRAM & 64GB+ system RAM

#6
post #5
post #4

I have a 5950x with 128 gb ram and a 12 gb 3060 gpu. The speed of generating tokens is excellent, the killer is that when the context grows even a little processing of it is super slow. Hopefully someone smart will optimize this, but as it is now I keep using other models like qwen, mistral and gemma.

I would so appreciate concrete data instead of subjectivities like "excellent" and "super slow". How many tokens is excellent? How many is super slow? How many is non-filled context?

People can read at a rate around 10 token/sec. So faster than that is pretty good, but it depends how wordy the response is (including chain of thought) and whether you'll be reading it all verbatim or just skimming.

Re: GPT-OSS-120B runs on just 8GB VRAM & 64GB+ system RAM

#7
post #5
post #4

I have a 5950x with 128 gb ram and a 12 gb 3060 gpu. The speed of generating tokens is excellent, the killer is that when the context grows even a little processing of it is super slow. Hopefully someone smart will optimize this, but as it is now I keep using other models like qwen, mistral and gemma.

I would so appreciate concrete data instead of subjectivities like "excellent" and "super slow". How many tokens is excellent? How many is super slow? How many is non-filled context?

I'm not really timing it as I just use these models via open webui, nvim and a few things I've made like a discord bot, everything going via ollama.

But for comparison, it is generating tokens about 1.5 times as fast as gemma 3 27B qat or mistral-small 2506 q4. Prompt processing/context however seems to be happening at about 1/4 of those models.

A bit more concrete of the "excellent", I can't really notice any difference between the speed of oss-120b once the context is processed and claude opus-4 via api.

Re: GPT-OSS-120B runs on just 8GB VRAM & 64GB+ system RAM

#8
post #5
post #4

I have a 5950x with 128 gb ram and a 12 gb 3060 gpu. The speed of generating tokens is excellent, the killer is that when the context grows even a little processing of it is super slow. Hopefully someone smart will optimize this, but as it is now I keep using other models like qwen, mistral and gemma.

I would so appreciate concrete data instead of subjectivities like "excellent" and "super slow". How many tokens is excellent? How many is super slow? How many is non-filled context?

Some numbers are posted in the comments:

> … you can expect the speed to half when going from 4k to 16k long prompt …

> … it did slow down somewhat (from 25T/s to 18T/s) for very long context …

Depends on the hardware configuration (size of VRAM, speed of CPU and system RAM) and llama.cpp parameter settings, a bigger context prompt slows the T/s number significantly but not order of magnitudes.

Facit: gpt-oss 120B on a small GPU is not the proper setup for chat use cases.

Re: GPT-OSS-120B runs on just 8GB VRAM & 64GB+ system RAM

#10
post #4

I have a 5950x with 128 gb ram and a 12 gb 3060 gpu. The speed of generating tokens is excellent, the killer is that when the context grows even a little processing of it is super slow. Hopefully someone smart will optimize this, but as it is now I keep using other models like qwen, mistral and gemma.

What are you aiming to do with these models that isn’t chat/text manipulation?
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