Baichuan M2 32B on Apple M2 Ultra (192 GB)

Can the Apple M2 Ultra (192 GB) run Baichuan M2 32B?

72 pts

Yes, it runs well

Runs well. At Q4_K_M it needs about 34.3 GB of 144 GB usable, with an estimated speed of 25.8 tok/s.

Best quant: Q4_K_M
Generation speed: 25.8 tok/s
Prompt processing: 515 tok/s
First token (1K prompt): 2.0 s
Largest safe context: 128K

Download Baichuan M2 32B

Recommended for the Apple M2 Ultra (192 GB): Q4_K_M. Every other quantization has its own download link in the table below.

Quantized files · GGUF

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

Download the Q4_K_M file (opens in a new tab)

huggingface.co/bartowski/baichuan-inc_Baichuan-M2-32B-GGUF (opens in a new tab)

ollama run hf.co/bartowski/baichuan-inc_Baichuan-M2-32B-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/baichuan-inc/Baichuan-M2-32B (opens in a new tab)

No sign-in needed.

Links checked 6 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.	29.9 / 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.	12.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	5.5 / 15
Total	72.5 / 100	72 ptsGreat (A) · 72 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 12.0/15, Memory use 5.5/15

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

Every quantization on the Apple M2 Ultra (192 GB)

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	27.4 GB	144 GB	42.4 tok/s	Runs well	66 ptsGood (B) · 66 of 100 points: Model quality 22.1/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 4.4/15	GGUF (opens in a new tab)
`Q3_K_M`	30.3 GB	144 GB	33.4 tok/s	Runs well	70 ptsGood (B) · 70 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 13.7/15, Memory use 4.9/15	GGUF (opens in a new tab)
`Q4_K_M`	34.3 GB	144 GB	25.8 tok/s	Runs well	72 ptsGreat (A) · 72 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 12.0/15, Memory use 5.5/15	GGUF (opens in a new tab)
`Q5_K_M`	37.5 GB	144 GB	21.3 tok/s	Runs well	72 ptsGreat (A) · 72 of 100 points: Model quality 30.6/45, Memory fit 25.0/25, Speed 11.0/15, Memory use 6.0/15	GGUF (opens in a new tab)
`Q6_K`	41.1 GB	144 GB	17.9 tok/s	Runs well	72 ptsGreat (A) · 72 of 100 points: Model quality 31.2/45, Memory fit 25.0/25, Speed 10.0/15, Memory use 6.6/15	GGUF (opens in a new tab)
`Q8_0`	49.0 GB	144 GB	13.1 tok/s	Runs well	72 ptsGreat (A) · 72 of 100 points: Model quality 31.5/45, Memory fit 25.0/25, Speed 8.6/15, Memory use 7.9/15	GGUF (opens in a new tab)
`F16`	79.8 GB	144 GB	6.1 tok/s	Runs well	75 ptsGreat (A) · 75 of 100 points: Model quality 31.5/45, Memory fit 25.0/25, Speed 5.8/15, Memory use 12.8/15	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

85 ptsExcellent (S) · 85 of 100 points: Model quality 31.2/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 Q6_K · 46.6 tok/s
83 ptsGreat (A) · 83 of 100 points: Model quality 29.9/45, Memory fit 24.0/25, Speed 14.6/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 Q4_K_M · 37.8 tok/s
82 ptsGreat (A) · 82 of 100 points: Model quality 29.9/45, Memory fit 24.0/25, Speed 14.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 Q4_K_M · 35.1 tok/s
82 ptsGreat (A) · 82 of 100 points: Model quality 29.9/45, Memory fit 24.0/25, Speed 13.2/15, Memory use 15.0/15 AMD Radeon RX 7900 XTX Q4_K_M · 31.0 tok/s
79 ptsGreat (A) · 79 of 100 points: Model quality 31.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.3/15 NVIDIA RTX PRO 6000 Blackwell Q6_K · 46.6 tok/s
77 ptsGreat (A) · 77 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 10.0/15, Memory use 12.8/15 Apple M4 Max (64 GB) Q4_K_M · 17.6 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.