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Can the Apple M2 Pro (32 GB) run Nemotron 3.5 Lightning 30B-A3B?

82 ptsGreat (A) · 82 of 100 points: Model quality 27.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, it runs well

Runs well. At Q3_K_M it needs about 17.8 GB of 21.4 GB usable, with an estimated speed of 45.6 tok/s.

Best quant
Q3_K_M
Generation speed
45.6 tok/s
Prompt processing
1,405 tok/s
First token (1K prompt)
0.7 s
Largest safe context
256K

Download Nemotron 3.5 Lightning 30B-A3B

Recommended for the Apple M2 Pro (32 GB): Q3_K_M. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run nemotron-3.5-lightning:30b

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q3_K_M (UD-Q3_K_XL) file (opens in a new tab)

huggingface.co/unsloth/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF (opens in a new tab)

ollama run hf.co/unsloth/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF:UD-Q3_K_XL

Original weights · Hugging Face

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

huggingface.co/nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16 (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 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.27.2 / 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
Total82.2 / 10082 ptsGreat (A) · 82 of 100 points: Model quality 27.2/45, 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 M2 Pro (32 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 15.0 GB 21.4 GB 57.9 tok/s Runs well 77 ptsGreat (A) · 77 of 100 points: Model quality 22.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (IQ2_M) (opens in a new tab)
Q3_K_M 17.8 GB 21.4 GB 45.6 tok/s Runs well 82 ptsGreat (A) · 82 of 100 points: Model quality 27.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (UD-Q3_K_XL) (opens in a new tab)
Q4_K_M 21.6 GB 21.4 GB 29.9 tok/s Runs with offload 68 ptsGood (B) · 68 of 100 points: Model quality 30.8/45, Memory fit 10.0/25, Speed 13.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 24.8 GB 21.4 GB 24.7 tok/s Heavy offload 62 ptsGood (B) · 62 of 100 points: Model quality 31.4/45, Memory fit 4.0/25, Speed 11.8/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 28.3 GB 21.4 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (UD-Q6_K_XL) (opens in a new tab)
Q8_0 35.8 GB 21.4 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 GGUF (opens in a new tab)
F16 65.5 GB 21.4 GB — Won't run 9 ptsWon't run (F) · 9 of 100 points: Model quality 9.7/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15 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.