Check my GPU

on

Can the Apple M3 Ultra (256 GB) run Llama 3.3 Nemotron Super 49B v1.5?

74 ptsGreat (A) · 74 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 9.9/15, Memory use 5.9/15

Yes, it runs well

Runs well. At Q4_K_M it needs about 49.1 GB of 192 GB usable, with an estimated speed of 17.4 tok/s.

Best quant
Q4_K_M
Generation speed
17.4 tok/s
Prompt processing
353 tok/s
First token (1K prompt)
2.9 s
Largest safe context
128K

Download Llama 3.3 Nemotron Super 49B v1.5

Recommended for the Apple M3 Ultra (256 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/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF:Q4_K_M

Original weights · Hugging Face

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

huggingface.co/nvidia/Llama-3_3-Nemotron-Super-49B-v1_5 (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 6 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.34.2 / 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.9.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.5.9 / 15
Total75.0 / 10074 ptsGreat (A) · 74 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 9.9/15, Memory use 5.9/15

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

Every quantization on the Apple M3 Ultra (256 GB)

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 38.6 GB 192 GB 28.5 tok/s Runs well 67 ptsGood (B) · 67 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 12.7/15, Memory use 4.6/15 GGUF (opens in a new tab)
Q3_K_M 43.1 GB 192 GB 22.4 tok/s Runs well 71 ptsGood (B) · 71 of 100 points: Model quality 30.2/45, Memory fit 25.0/25, Speed 11.2/15, Memory use 5.2/15 GGUF (opens in a new tab)
Q4_K_M 49.1 GB 192 GB 17.4 tok/s Runs well 74 ptsGreat (A) · 74 of 100 points: Model quality 34.2/45, Memory fit 25.0/25, Speed 9.9/15, Memory use 5.9/15 GGUF (opens in a new tab)
Q5_K_M 54.1 GB 192 GB 14.3 tok/s Runs well 75 ptsGreat (A) · 75 of 100 points: Model quality 34.9/45, Memory fit 25.0/25, Speed 9.0/15, Memory use 6.5/15 GGUF (opens in a new tab)
Q6_K 59.5 GB 192 GB 12.0 tok/s Runs well 76 ptsGreat (A) · 76 of 100 points: Model quality 35.6/45, Memory fit 25.0/25, Speed 8.2/15, Memory use 7.2/15 GGUF (opens in a new tab)
Q8_0 71.5 GB 192 GB 8.8 tok/s Runs well 76 ptsGreat (A) · 76 of 100 points: Model quality 36.0/45, Memory fit 25.0/25, Speed 7.1/15, Memory use 8.6/15 GGUF · folder (opens in a new tab)
F16 118 GB 192 GB 4.1 tok/s Runs well 80 ptsGreat (A) · 80 of 100 points: Model quality 36.0/45, Memory fit 25.0/25, Speed 4.8/15, Memory use 14.2/15 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.