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Can the Apple M2 Pro (32 GB) run LFM2 8B-A1B?

66 ptsGood (B) · 66 of 100 points: Model quality 16.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.0/15

Yes, it runs well

Runs well. At Q6_K it needs about 9.3 GB of 21.4 GB usable, with an estimated speed of 48.9 tok/s.

Best quant
Q6_K
Generation speed
48.9 tok/s
Prompt processing
2,811 tok/s
First token (1K prompt)
0.4 s
Largest safe context
125K

Download LFM2 8B-A1B

Recommended for the Apple M2 Pro (32 GB): Q6_K. 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 Q6_K file (opens in a new tab)

huggingface.co/LiquidAI/LFM2-8B-A1B-GGUF (opens in a new tab)

ollama run hf.co/LiquidAI/LFM2-8B-A1B-GGUF:Q6_K

Original weights · Hugging Face

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

huggingface.co/LiquidAI/LFM2-8B-A1B (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/Liquid4All/cookbook (opens in a new tab)

git clone https://github.com/Liquid4All/cookbook

Liquid AI’s official cookbook for running LFM models.

Links checked 1 week ago against Hugging Face, Ollama and GitHub.

How the B 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.16.9 / 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.15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.10.0 / 15
Total66.9 / 10066 ptsGood (B) · 66 of 100 points: Model quality 16.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.0/15

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

Every quantization on the Apple M2 Pro (32 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.8 GB 21.4 GB 116 tok/s Runs well 58 ptsGood (B) · 58 of 100 points: Model quality 12.0/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.2/15 GGUF files (opens in a new tab)
Q3_K_M 6.5 GB 21.4 GB 91.1 tok/s Runs well 61 ptsGood (B) · 61 of 100 points: Model quality 14.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.0/15 GGUF files (opens in a new tab)
Q4_K_M 7.5 GB 21.4 GB 70.5 tok/s Runs well 64 ptsGood (B) · 64 of 100 points: Model quality 16.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.1/15 GGUF (opens in a new tab)
Q5_K_M 8.3 GB 21.4 GB 58.2 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 16.6/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 9.0/15 GGUF (opens in a new tab)
Q6_K 9.3 GB 21.4 GB 48.9 tok/s Runs well 66 ptsGood (B) · 66 of 100 points: Model quality 16.9/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.0/15 GGUF (opens in a new tab)
Q8_0 11.3 GB 21.4 GB 35.9 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 17.1/45, Memory fit 25.0/25, Speed 14.2/15, Memory use 12.1/15 GGUF (opens in a new tab)
F16 19.1 GB 21.4 GB 16.5 tok/s Tight fit 65 ptsGood (B) · 65 of 100 points: Model quality 17.1/45, Memory fit 24.0/25, Speed 9.6/15, Memory use 15.0/15 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

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.