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Can the Apple M2 (16 GB) run UI-Mate 9B?

77 ptsGreat (A) · 77 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 7.9/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 7.5 GB of 10.7 GB usable, with an estimated speed of 11.2 tok/s.

Best quant
Q4_K_M
Generation speed
11.2 tok/s
Prompt processing
237 tok/s
First token (1K prompt)
4.3 s
Largest safe context
104K

Download UI-Mate 9B

Recommended for the Apple M2 (16 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/tencent_UI-Mate-9B-GGUF (opens in a new tab)

ollama run hf.co/bartowski/tencent_UI-Mate-9B-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/tencent/UI-Mate-9B (opens in a new tab)

No sign-in needed.

Links checked 1 week 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.7.9 / 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
Total77.9 / 10077 ptsGreat (A) · 77 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 7.9/15, 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 M2 (16 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 5.5 GB 10.7 GB 18.4 tok/s Runs well 69 ptsGood (B) · 69 of 100 points: Model quality 22.1/45, Memory fit 25.0/25, Speed 10.2/15, Memory use 11.8/15 GGUF (opens in a new tab)
Q3_K_M 6.3 GB 10.7 GB 14.5 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 9.0/15, Memory use 13.6/15 GGUF (opens in a new tab)
Q4_K_M 7.5 GB 10.7 GB 11.2 tok/s Runs well 77 ptsGreat (A) · 77 of 100 points: Model quality 29.9/45, Memory fit 25.0/25, Speed 7.9/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 8.4 GB 10.7 GB 9.3 tok/s Runs well 77 ptsGreat (A) · 77 of 100 points: Model quality 30.6/45, Memory fit 25.0/25, Speed 7.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 9.4 GB 10.7 GB 7.8 tok/s Tight fit 76 ptsGreat (A) · 76 of 100 points: Model quality 31.2/45, Memory fit 24.0/25, Speed 6.6/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 11.7 GB 10.7 GB 4.9 tok/s Runs with offload 61 ptsGood (B) · 61 of 100 points: Model quality 31.5/45, Memory fit 10.0/25, Speed 5.2/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 20.5 GB 10.7 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.5/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.