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Can the Apple M4 Max (64 GB) run Ling Mini 2.0 (16B-A1.4B)?

78 ptsGreat (A) · 78 of 100 points: Model quality 23.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At F16 it needs about 37.3 GB of 48.0 GB usable, with an estimated speed of 48.4 tok/s.

Best quant
F16
Generation speed
48.4 tok/s
Prompt processing
8,149 tok/s
First token (1K prompt)
0.3 s
Largest safe context
32K

Download Ling Mini 2.0 (16B-A1.4B)

Recommended for the Apple M4 Max (64 GB): F16. 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 F16 file (opens in a new tab)

huggingface.co/inclusionAI/Ling-mini-2.0-GGUF (opens in a new tab)

ollama run hf.co/inclusionAI/Ling-mini-2.0-GGUF:BF16

Original weights · Hugging Face

Full-precision files from Ant Group (inclusionAI), for fine-tuning, vLLM, Transformers.

huggingface.co/inclusionAI/Ling-mini-2.0 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Ant Group (inclusionAI). The weights themselves download from Hugging Face.

github.com/inclusionAI/Ling-V2 (opens in a new tab)

git clone https://github.com/inclusionAI/Ling-V2

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.23.4 / 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
Total78.4 / 10078 ptsGreat (A) · 78 of 100 points: Model quality 23.4/45, 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 M4 Max (64 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 11.3 GB 48.0 GB 339 tok/s Runs well 61 ptsGood (B) · 61 of 100 points: Model quality 16.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 5.4/15 GGUF (opens in a new tab)
Q3_K_M 12.7 GB 48.0 GB 266 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 19.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 6.1/15 GGUF files (opens in a new tab)
Q4_K_M 14.7 GB 48.0 GB 206 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 22.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.1/15 GGUF (opens in a new tab)
Q5_K_M 16.3 GB 48.0 GB 170 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 7.8/15 GGUF files (opens in a new tab)
Q6_K 18.1 GB 48.0 GB 143 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 23.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 8.7/15 GGUF (opens in a new tab)
Q8_0 22.0 GB 48.0 GB 105 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 23.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 10.6/15 GGUF (opens in a new tab)
F16 37.3 GB 48.0 GB 48.4 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 23.4/45, 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)

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.