Comment by spullara
1 day ago
On flights with shitty wifi I have been running gpt-oss:120b on my macbook using ollama. Ok model for coding if you can't reach a good one.
1 day ago
On flights with shitty wifi I have been running gpt-oss:120b on my macbook using ollama. Ok model for coding if you can't reach a good one.
GPT-OSS-120b/20b is probably the best you can run on your own hardware today. Be careful with the quantized versions though, as they're really horrible compared to the native MXFP4. I haven't looked in this particular case, but Ollama tends to hide their quantizations for some reason, so most people who could be running 20B with MXFP4, are still on Q8 and getting much worse results than they could.
The gpt-oss weights on Ollama are native mxfp4 (the same weights provided by OpenAI). No additional quantization is applied, so let me know if you're seeing any strange results with Ollama.
Most gpt-oss GGUF files online have parts of their weights quantized to q8_0, and we've seen folks get some strange results from these models. If you're importing these to Ollama to run, the output quality may decrease.
they support that format according to the model page on their site:
https://ollama.com/library/gpt-oss
What’s the distinction between MXP4 and Q8 exactly?
It's a different way of doing quantization (https://huggingface.co/docs/transformers/en/quantization/mxf...) but I think the most important thing is that OpenAI delivered their own quantization (the MXFP4 from OpenAI/GPT-OSS on HuggingFace, guaranteed correct) whereas all the Q8 and other quantizations you see floating around are community efforts, with somewhat uneven results depending on who done it.
Concretely from my testing, both 20B and 120B has a lot higher refusal rate with Q8 compared to MXFP4, and lower quality responses overall. But don't take my word for it, the 20B weights are tiny and relatively effortless to try both versions and compare yourself.
6 replies →
Should be a bit faster if you run an MLX version of the model with LM Studio instead. Ollama doesn't support MLX.
Qwen3-Coder is in the same ballpark and maybe a bit better at coding
LM Studio will run dynamic quants from Unsloth too. Much nicer than Ollama.
The key thing I'm confident in is that 2-3 years from now there's going to be a model(s) and workflow that has comparable accuracy, perhaps noticeable (but tolerable) higher latency that can be run locally. There's just no reason to believe this isn't achievable.
Hard to understand how this won't make all of the solutions for existing use cases commodity. I'm sure 2-3 years from now there'll be stuff that seems like magic to us now -- but it will be more-meta, more "here's a hypothesis of a strategically valuable outcome and heres a solution (with market research and user testing done".
I think current performance and leading models will turn out to have been terrible indicators for future market leader (and my money will remain on the incumbents with the largest cash reserves (namely Google) that have invested in fundamental research and scaling).
Could you share which Macbook model? And what context size you're getting.
I just checked gpt-oss:20b on my M4 Pro 24GB, and got 400.67 tokens/s on input and 46.53 tokens/s on output. That's for a tiny context of 72 tokens.
Are you running the full 65GB model on a MacBook Pro? What tokens per second do you get? What specs? M5?
I am running the full model on an 128GB M3 Max.
On an m4 pro 128gb: 75 t/s.
Caveat: That's just for the first prompt.
If they're running 120B on a M5 (32GB max of memory today), I'd like to know how.
Probably an M4 which has up to 128GB currently
That must be a beefed up MacBook (or you must be quite patient).
gpt-oss:20b on my M1 MBP is usable but quite slow.