Olmo 3.1 32B Think on Apple M4 Pro (48 GB)

Can the Apple M4 Pro (48 GB) run Olmo 3.1 32B Think?

79 pts

Yes, it runs well

Runs well. At Q5_K_M it needs about 27.1 GB of 36.0 GB usable, with an estimated speed of 7.4 tok/s.

Best quant: Q5_K_M
Generation speed: 7.4 tok/s
Prompt processing: 177 tok/s
First token (1K prompt): 5.8 s
Largest safe context: 64K

Download Olmo 3.1 32B Think

Recommended for the Apple M4 Pro (48 GB): Q5_K_M. 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 olmo-3.1:32b-think

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 Q5_K_M file (opens in a new tab)

huggingface.co/unsloth/Olmo-3.1-32B-Think-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Olmo-3.1-32B-Think-GGUF:Q5_K_M

Original weights · Hugging Face

Full-precision files from Ai2, for fine-tuning, vLLM, Transformers.

huggingface.co/allenai/Olmo-3.1-32B-Think (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Ai2. The weights themselves download from Hugging Face.

github.com/allenai/OLMo (opens in a new tab)

git clone https://github.com/allenai/OLMo

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

How the A 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.	33.2 / 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.	6.5 / 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	79.6 / 100	79 ptsGreat (A) · 79 of 100 points: Model quality 33.2/45, Memory fit 25.0/25, Speed 6.5/15, 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 M4 Pro (48 GB)

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	17.1 GB	36.0 GB	14.7 tok/s	Runs well	69 ptsGood (B) · 69 of 100 points: Model quality 23.9/45, Memory fit 25.0/25, Speed 9.1/15, Memory use 11.0/15	GGUF (opens in a new tab)
`Q3_K_M`	20.0 GB	36.0 GB	11.6 tok/s	Runs well	74 ptsGreat (A) · 74 of 100 points: Model quality 28.7/45, Memory fit 25.0/25, Speed 8.1/15, Memory use 12.8/15	GGUF (opens in a new tab)
`Q4_K_M`	23.9 GB	36.0 GB	9.0 tok/s	Runs well	79 ptsGreat (A) · 79 of 100 points: Model quality 32.5/45, Memory fit 25.0/25, Speed 7.1/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q5_K_M`	27.1 GB	36.0 GB	7.4 tok/s	Runs well	79 ptsGreat (A) · 79 of 100 points: Model quality 33.2/45, Memory fit 25.0/25, Speed 6.5/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q6_K`	30.6 GB	36.0 GB	6.2 tok/s	Tight fit	78 ptsGreat (A) · 78 of 100 points: Model quality 33.9/45, Memory fit 24.0/25, Speed 5.9/15, Memory use 15.0/15	GGUF (opens in a new tab)
`Q8_0`	38.4 GB	36.0 GB	3.9 tok/s	Runs with offload	63 ptsGood (B) · 63 of 100 points: Model quality 34.2/45, Memory fit 10.0/25, Speed 4.7/15, Memory use 15.0/15	GGUF (opens in a new tab)
`F16`	68.7 GB	36.0 GB	—	Won't run	10 ptsWon't run (F) · 10 of 100 points: Model quality 10.3/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF · folder (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

88 ptsExcellent (S) · 88 of 100 points: Model quality 33.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 Q5_K_M · 56.4 tok/s
86 ptsExcellent (S) · 86 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 14.7/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 Q4_K_M · 38.4 tok/s
85 ptsExcellent (S) · 85 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 14.2/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 Q4_K_M · 35.6 tok/s
84 ptsGreat (A) · 84 of 100 points: Model quality 32.5/45, Memory fit 24.0/25, Speed 13.3/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX Q4_K_M · 31.5 tok/s
81 ptsGreat (A) · 81 of 100 points: Model quality 33.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15 NVIDIA RTX PRO 6000 Blackwell Q6_K · 47.4 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.