NorthCUA Mini on NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

Can the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition run NorthCUA Mini?

Yes, it runs great

Runs great. At F16 it needs about 75.8 GB of 96.0 GB usable, with an estimated speed of 98.2 tok/s.

Best quant: F16
Generation speed: 98.2 tok/s
Prompt processing: 26,400 tok/s
First token (1K prompt): 0.3 s
Largest safe context: 256K

Best app to run it on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

On Windows

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/…

On Linux

vLLM

Inference server · runs on vLLM

The full model fits in GPU memory at 8-bit, so it can batch many requests: best for serving a team or an app.

vllm serve beipo-ai/NorthCUA-Mini

LM Studio Point-and-click: search, download and chat in one window.
llama.cpp Usually the first to support new model architectures.

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 NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition: F16. 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 F16 files (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

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 F16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at F16, 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 NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition: 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 everything sits in GPU 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
Match score	81.6 / 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 NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	19.6 GB	96.0 GB	253 tok/s	Runs great	41Fair quality (C) · 41 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 63/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	22.8 GB	96.0 GB	233 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 67/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	27.0 GB	96.0 GB	211 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 71/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	30.5 GB	96.0 GB	194 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 73/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	34.4 GB	96.0 GB	179 tok/s	Runs great	58Good quality (B) · 58 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	42.8 GB	96.0 GB	154 tok/s	Runs great	59Good quality (B) · 59 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	75.8 GB	96.0 GB	98.2 tok/s	Runs great	59Good quality (B) · 59 of 100: how capable the model is at F16, whatever it runs on. Match score 81/100 (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

59Good quality (B) · 59 of 100: how capable the model is at F16, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA RTX PRO 6000 Blackwell F16 · 98.2 tok/s · Runs great
58Good quality (B) · 58 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Apple M1 Max (64 GB) Q6_K · 72.3 tok/s · Runs great
58Good quality (B) · 58 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 81/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Max (64 GB) Q6_K · 90.1 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 80/100 (quality, fit, speed and memory use) sets the ranking. Apple M4 Pro (48 GB) Q5_K_M · 62.2 tok/s · Runs great
58Good quality (B) · 58 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 80/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 Q6_K · 179 tok/s · Runs well (tight fit)
59Good quality (B) · 59 of 100: how capable the model is at F16, whatever it runs on. Match score 79/100 (quality, fit, speed and memory use) sets the ranking. Apple M2 Ultra (192 GB) F16 · 51.3 tok/s · Runs great

Quick answers

How much memory does NorthCUA Mini need on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

At F16 it needs about 75.8 GB. The NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB has 96.0 GB usable.

How fast is NorthCUA Mini on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

About 98.2 tok/s at F16, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of NorthCUA Mini should I use on the NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition 96GB?

F16. 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.