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Can the Apple M5 Ultra (96 GB) run NorthCUA Mini?

59Good quality (B) · 59 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q8_0 it needs about 43.7 GB of 72.0 GB usable, with an estimated speed of 118 tok/s.

Best quant
Q8_0
Generation speed
118 tok/s
Prompt processing
7,791 tok/s
First token (1K prompt)
0.3 s
Largest safe context
256K

Best app to run it on the Apple M5 Ultra (96 GB)

On macOS

LM Studio

Desktop app · runs on llama.cpp, MLX

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

Open NorthCUA Mini in LM Studio

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

Want an agent? NorthCUA Mini supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

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 NorthCUA Mini

Recommended for the Apple M5 Ultra (96 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 file (opens in a new tab)

huggingface.co/bartowski/beipo-ai_NorthCUA-Mini-GGUF (opens in a new tab)

ollama run hf.co/bartowski/beipo-ai_NorthCUA-Mini-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/beipo-ai/NorthCUA-Mini (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 59Good quality (B) · 59 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (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 M5 Ultra (96 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.26.6 / 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.14.0 / 15
Match score80.6 / 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 M5 Ultra (96 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 20.6 GB 72.0 GB 219 tok/s Runs great 41Fair quality (C) · 41 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 23.7 GB 72.0 GB 197 tok/s Runs great 49Fair quality (C) · 49 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 27.9 GB 72.0 GB 175 tok/s Runs great 56Good quality (B) · 56 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q5_K_M 31.5 GB 72.0 GB 157 tok/s Runs great 57Good quality (B) · 57 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 75/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 35.3 GB 72.0 GB 143 tok/s Runs great 58Good quality (B) · 58 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 43.7 GB 72.0 GB 118 tok/s Runs great 59Good quality (B) · 59 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 76.7 GB 72.0 GB 58.8 tok/s Runs wellover GPU memory limit 59Good quality (B) · 59 of 100: how capable the model is at F16, whatever it runs on. Match score 66/100 (quality, fit, speed and memory use) sets the ranking. 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

Quick answers

How much memory does NorthCUA Mini need on the M5 Ultra 96GB?

At Q8_0 it needs about 43.7 GB. The M5 Ultra 96GB has 72.0 GB usable.

How fast is NorthCUA Mini on the M5 Ultra 96GB?

About 118 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of NorthCUA Mini should I use on the M5 Ultra 96GB?

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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