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Can the Apple M2 Ultra (64 GB) run Llama 3.1 Nemotron Nano 8B v1?

54Fair quality (C) · 54 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 14.0 GB of 48.0 GB usable, with an estimated speed of 53.6 tok/s.

Best quant
Q8_0
Generation speed
53.6 tok/s
Prompt processing
2,100 tok/s
First token (1K prompt)
0.5 s
Largest safe context
128K

Best app to run it on the Apple M2 Ultra (64 GB)

On macOS

LM Studio

Desktop app · runs on llama.cpp, MLX

  • Point-and-click: search, download and chat in one window.

Open Llama 3.1 Nemotron Nano 8B v1 in LM Studio

  • llama.cpp Usually the first to support new model architectures.
  • Ollama Runs the Hugging Face GGUF with ollama run hf.co/…

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Llama 3.1 Nemotron Nano 8B v1

Recommended for the Apple M2 Ultra (64 GB): Q8_0. 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 Q8_0 (UD-Q8_K_XL) file (opens in a new tab)

huggingface.co/unsloth/Llama-3.1-Nemotron-Nano-8B-v1-GGUF (opens in a new tab)

ollama run hf.co/unsloth/Llama-3.1-Nemotron-Nano-8B-v1-GGUF:UD-Q8_K_XL

Original weights · Hugging Face

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

huggingface.co/nvidia/Llama-3.1-Nemotron-Nano-8B-v1 (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 54Fair quality (C) · 54 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q8_0, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great
How well it runs on the Apple M2 Ultra (64 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.24.3 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.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.6.7 / 15
Match score71.0 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Apple M2 Ultra (64 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 8.7 GB 48.0 GB 173 tok/s Runs great 37Basic quality (D) · 37 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 9.4 GB 48.0 GB 136 tok/s Runs great 45Fair quality (C) · 45 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 64/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 10.4 GB 48.0 GB 105 tok/s Runs great 51Fair quality (C) · 51 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 11.2 GB 48.0 GB 87.0 tok/s Runs great 52Fair quality (C) · 52 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 68/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 12.1 GB 48.0 GB 73.1 tok/s Runs great 53Fair quality (C) · 53 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 14.0 GB 48.0 GB 53.6 tok/s Runs great 54Fair quality (C) · 54 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. GGUF (UD-Q8_K_XL) (opens in a new tab)
F16 21.6 GB 48.0 GB 24.7 tok/s Runs well 54Fair quality (C) · 54 of 100: how capable the model is at F16, whatever it runs on. Match score 71/100 (quality, fit, speed and memory use) sets the ranking. 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

Quick answers

How much memory does Llama 3.1 Nemotron Nano 8B v1 need on the M2 Ultra 64GB?

At Q8_0 it needs about 14.0 GB. The M2 Ultra 64GB has 48.0 GB usable.

How fast is Llama 3.1 Nemotron Nano 8B v1 on the M2 Ultra 64GB?

About 53.6 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Llama 3.1 Nemotron Nano 8B v1 should I use on the M2 Ultra 64GB?

Q8_0. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.

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