Can the Apple M5 Max (128 GB) run gpt-oss 120B?

92 pts

Yes, it runs well

Runs well. At MXFP4 it needs about 75.2 GB of 96.0 GB usable, with an estimated speed of 68.1 tok/s.

Best quant: MXFP4
Generation speed: 68.1 tok/s
Prompt processing: 2,298 tok/s
First token (1K prompt): 0.4 s
Largest safe context: 128K

Download gpt-oss 120B

Recommended for the Apple M5 Max (128 GB): MXFP4. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run gpt-oss:120b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the MXFP4 file (opens in a new tab)

huggingface.co/ggml-org/gpt-oss-120b-GGUF (opens in a new tab)

ollama run hf.co/ggml-org/gpt-oss-120b-GGUF:MXFP4

Original weights · Hugging Face

Full-precision files from OpenAI, for fine-tuning, vLLM, Transformers.

huggingface.co/openai/gpt-oss-120b (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from OpenAI. The weights themselves download from Hugging Face.

github.com/openai/gpt-oss (opens in a new tab)

git clone https://github.com/openai/gpt-oss

Links checked 7 days ago against Hugging Face, Ollama and GitHub.

How the S grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	37.8 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	15.0 / 15
Total	92.8 / 100	92 ptsExcellent (S) · 92 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Apple M5 Max (128 GB)

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`MXFP4`	75.2 GB	96.0 GB	68.1 tok/s	Runs well	92 ptsExcellent (S) · 92 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15	GGUF (opens in a new tab) Original weights (opens in a new tab)
`F16`	242 GB	96.0 GB	—	Won't run	11 ptsWon't run (F) · 11 of 100 points: Model quality 11.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab) Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

92 ptsExcellent (S) · 92 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 Apple M4 Max (128 GB) MXFP4 · 60.6 tok/s
92 ptsExcellent (S) · 92 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA RTX PRO 6000 Blackwell MXFP4 · 231 tok/s
91 ptsExcellent (S) · 91 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 14.1/15, Memory use 15.0/15 NVIDIA GB10 (128 GB) MXFP4 · 35.2 tok/s
90 ptsExcellent (S) · 90 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 12.7/15 Apple M2 Ultra (192 GB) MXFP4 · 88.7 tok/s
90 ptsExcellent (S) · 90 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 12.6/15, Memory use 15.0/15 AMD Ryzen AI Max+ 395 (128 GB) MXFP4 · 28.4 tok/s
87 ptsExcellent (S) · 87 of 100 points: Model quality 37.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.0/15 Apple M3 Ultra (256 GB) MXFP4 · 90.8 tok/s

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.