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Can the Apple M2 Pro (32 GB) run Nemotron Nano 9B v2?

70 ptsGood (B) · 70 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 11.6/15, Memory use 8.5/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 7.9 GB of 21.4 GB usable, with an estimated speed of 23.8 tok/s.

Best quant
Q4_K_M
Generation speed
23.8 tok/s
Prompt processing
474 tok/s
First token (1K prompt)
2.2 s
Largest safe context
128K

Download Nemotron Nano 9B v2

Recommended for the Apple M2 Pro (32 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/nvidia_NVIDIA-Nemotron-Nano-9B-v2-GGUF (opens in a new tab)

ollama run hf.co/bartowski/nvidia_NVIDIA-Nemotron-Nano-9B-v2-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/nvidia/NVIDIA-Nemotron-Nano-9B-v2 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

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

github.com/NVIDIA-NeMo/Nemotron (opens in a new tab)

git clone https://github.com/NVIDIA-NeMo/Nemotron

NVIDIA’s Nemotron repo with recipes for the whole family.

Links checked 7 days 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.25.7 / 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.11.6 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.8.5 / 15
Total70.7 / 10070 ptsGood (B) · 70 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 11.6/15, Memory use 8.5/15

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 6.0 GB 21.4 GB 39.1 tok/s Runs well 65 ptsGood (B) · 65 of 100 points: Model quality 18.9/45, Memory fit 25.0/25, Speed 14.8/15, Memory use 6.5/15 GGUF (opens in a new tab)
Q3_K_M 6.8 GB 21.4 GB 30.7 tok/s Runs well 68 ptsGood (B) · 68 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 13.2/15, Memory use 7.3/15 GGUF (opens in a new tab)
Q4_K_M 7.9 GB 21.4 GB 23.8 tok/s Runs well 70 ptsGood (B) · 70 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 11.6/15, Memory use 8.5/15 GGUF (opens in a new tab)
Q5_K_M 8.8 GB 21.4 GB 19.6 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 26.2/45, Memory fit 25.0/25, Speed 10.5/15, Memory use 9.4/15 GGUF (opens in a new tab)
Q6_K 9.7 GB 21.4 GB 16.5 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 26.7/45, Memory fit 25.0/25, Speed 9.6/15, Memory use 10.5/15 GGUF (opens in a new tab)
Q8_0 11.9 GB 21.4 GB 12.1 tok/s Runs well 73 ptsGreat (A) · 73 of 100 points: Model quality 27.0/45, Memory fit 25.0/25, Speed 8.3/15, Memory use 12.8/15 GGUF (opens in a new tab)
F16 20.2 GB 21.4 GB 5.6 tok/s Tight fit 71 ptsGood (B) · 71 of 100 points: Model quality 27.0/45, Memory fit 24.0/25, Speed 5.6/15, 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.