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Can the Apple M2 Ultra (192 GB) run Kimi Linear 48B-A3B?

83 ptsGreat (A) · 83 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.3/15

Yes, it runs well

Runs well. At Q8_0 it needs about 64.3 GB of 144 GB usable, with an estimated speed of 71.7 tok/s.

Best quant
Q8_0
Generation speed
71.7 tok/s
Prompt processing
5,621 tok/s
First token (1K prompt)
0.3 s
Largest safe context
1,024K

Download Kimi Linear 48B-A3B

Recommended for the Apple M2 Ultra (192 GB): Q8_0. 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 Q8_0 files (opens in a new tab)

huggingface.co/bartowski/moonshotai_Kimi-Linear-48B-A3B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/bartowski/moonshotai_Kimi-Linear-48B-A3B-Instruct-GGUF

Original weights · Hugging Face

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

huggingface.co/moonshotai/Kimi-Linear-48B-A3B-Instruct (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/MoonshotAI/Kimi-Linear (opens in a new tab)

git clone https://github.com/MoonshotAI/Kimi-Linear

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.33.3 / 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.10.3 / 15
Total83.6 / 10083 ptsGreat (A) · 83 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.3/15

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 31.8 GB 144 GB 231 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 23.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.1/15 GGUF (opens in a new tab)
Q3_K_M 36.3 GB 144 GB 182 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 28.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.8/15 GGUF (opens in a new tab)
Q4_K_M 42.1 GB 144 GB 141 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 31.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.8/15 GGUF (opens in a new tab)
Q5_K_M 47.1 GB 144 GB 116 tok/s Runs well 79 ptsGreat (A) · 79 of 100 points: Model quality 32.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.5/15 GGUF (opens in a new tab)
Q6_K 52.5 GB 144 GB 97.8 tok/s Runs well 81 ptsGreat (A) · 81 of 100 points: Model quality 33.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.4/15 GGUF (opens in a new tab)
Q8_0 64.3 GB 144 GB 71.7 tok/s Runs well 83 ptsGreat (A) · 83 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.3/15 GGUF · folder (opens in a new tab)
F16 110 GB 144 GB 33.1 tok/s Runs well 86 ptsExcellent (S) · 86 of 100 points: Model quality 33.3/45, Memory fit 25.0/25, Speed 13.6/15, Memory use 15.0/15 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

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.