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Can the NVIDIA GeForce RTX 3090 run North Mini Code 1.0 (30B-A3B)?

88 ptsExcellent (S) · 88 of 100 points: Model quality 34.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15

Yes, with little memory to spare

Tight fit. At Q5_K_M it needs about 23.7 GB of 24.0 GB usable, with an estimated speed of 158 tok/s.

Best quant
Q5_K_M
Generation speed
158 tok/s
Prompt processing
8,544 tok/s
First token (1K prompt)
0.3 s
Largest safe context
14K

Download North Mini Code 1.0 (30B-A3B)

Recommended for the NVIDIA GeForce RTX 3090: Q5_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 Q5_K_M file (opens in a new tab)

huggingface.co/unsloth/North-Mini-Code-1.0-GGUF (opens in a new tab)

ollama run hf.co/unsloth/North-Mini-Code-1.0-GGUF:Q5_K_M

Original weights · Hugging Face

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

huggingface.co/CohereLabs/North-Mini-Code-1.0 (opens in a new tab)

No sign-in needed.

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

How the S 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.34.9 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.24.0 / 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
Total88.9 / 10088 ptsExcellent (S) · 88 of 100 points: Model quality 34.9/45, Memory fit 24.0/25, 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 NVIDIA GeForce RTX 3090

Quant Memory needed Available Speed Fit Grade · points Download
Q2_K 14.3 GB 24.0 GB 314 tok/s Runs well 78 ptsGreat (A) · 78 of 100 points: Model quality 25.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.7/15 GGUF (UD-Q2_K_XL) (opens in a new tab)
Q3_K_M 17.0 GB 24.0 GB 248 tok/s Runs well 85 ptsExcellent (S) · 85 of 100 points: Model quality 30.2/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q4_K_M 20.7 GB 24.0 GB 191 tok/s Tight fit 88 ptsExcellent (S) · 88 of 100 points: Model quality 34.2/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q5_K_M 23.7 GB 24.0 GB 158 tok/s Tight fit 88 ptsExcellent (S) · 88 of 100 points: Model quality 34.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q6_K 27.1 GB 24.0 GB 50.8 tok/s Runs with offload 75 ptsGreat (A) · 75 of 100 points: Model quality 35.6/45, Memory fit 10.0/25, Speed 15.0/15, Memory use 15.0/15 GGUF (opens in a new tab)
Q8_0 34.4 GB 24.0 GB 18.7 tok/s Heavy offload 65 ptsGood (B) · 65 of 100 points: Model quality 36.0/45, Memory fit 4.0/25, Speed 10.3/15, Memory use 15.0/15 GGUF (opens in a new tab)
F16 63.1 GB 24.0 GB — Won't run 10 ptsWon't run (F) · 10 of 100 points: Model quality 10.8/45, 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.