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LLMs ·

Ministral 3 14B Reasoning

Parameters
14B
Architecture
Dense
Max context
256K tokens
License
apache-2.0
Released
2025-12

How to run it

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512-GGUF (opens in a new tab)

ollama run hf.co/mistralai/Ministral-3-14B-Reasoning-2512-GGUF

Original weights · Hugging Face

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

huggingface.co/mistralai/Ministral-3-14B-Reasoning-2512 (opens in a new tab)

No sign-in needed.

Official repo · GitHub

Reference code, inference scripts and docs from Mistral AI. The weights themselves download from Hugging Face.

github.com/mistralai/mistral-inference (opens in a new tab)

git clone https://github.com/mistralai/mistral-inference

Mistral AI’s official inference code.

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

Memory needed

Weights plus KV cache and runtime overhead. Leave a little extra free for the operating system and other apps.

Quantization Quality Weights Total at 8K context Total at 32K context Download
Q2_K Low 5.6 GB 7.5 GB 11.5 GB GGUF files (opens in a new tab)
Q3_K_M Low 6.8 GB 8.8 GB 12.8 GB GGUF files (opens in a new tab)
Q4_K_M Good 8.5 GB 10.5 GB 14.5 GB GGUF (opens in a new tab)
Q5_K_M High 9.9 GB 11.8 GB 15.9 GB GGUF (opens in a new tab)
Q6_K High 11.4 GB 13.4 GB 17.4 GB GGUF files (opens in a new tab)
Q8_0 Very high 14.8 GB 16.7 GB 20.8 GB GGUF (opens in a new tab)
F16 Full 27.9 GB 29.8 GB 33.9 GB GGUF (opens in a new tab) Original weights (opens in a new tab)

Best hardware for Ministral 3 14B Reasoning

The least expensive GPU in our catalog that runs it without offloading is the Intel Arc A580 (launch price $179) at Q2_K.

Grade · points Hardware Quant Memory Speed Fit
84 ptsGreat (A) · 84 of 100 points: Model quality 29.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 3090 Q8_0 17.9 GB / 24.0 GB 41.9 tok/s Runs well
84 ptsGreat (A) · 84 of 100 points: Model quality 29.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4090 Q8_0 17.9 GB / 24.0 GB 45.2 tok/s Runs well
83 ptsGreat (A) · 83 of 100 points: Model quality 28.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 AMD Radeon RX 9070 XT Q5_K_M 12.6 GB / 16.0 GB 40.1 tok/s Runs well
83 ptsGreat (A) · 83 of 100 points: Model quality 28.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4070 Ti SUPER Q5_K_M 12.6 GB / 16.0 GB 48.9 tok/s Runs well
83 ptsGreat (A) · 83 of 100 points: Model quality 28.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 4080 SUPER Q5_K_M 12.6 GB / 16.0 GB 53.5 tok/s Runs well
83 ptsGreat (A) · 83 of 100 points: Model quality 28.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5070 Ti Q5_K_M 12.6 GB / 16.0 GB 65.1 tok/s Runs well
83 ptsGreat (A) · 83 of 100 points: Model quality 28.8/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5080 Q5_K_M 12.6 GB / 16.0 GB 69.8 tok/s Runs well
83 ptsGreat (A) · 83 of 100 points: Model quality 29.4/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 14.0/15 AMD Radeon RX 7900 XTX Q6_K 14.6 GB / 24.0 GB 50.5 tok/s Runs well
83 ptsGreat (A) · 83 of 100 points: Model quality 28.2/45, Memory fit 25.0/25, Speed 14.9/15, Memory use 15.0/15 NVIDIA GeForce RTX 5060 Ti 16GB Q4_K_M 11.3 GB / 16.0 GB 39.4 tok/s Runs well
82 ptsGreat (A) · 82 of 100 points: Model quality 29.7/45, Memory fit 25.0/25, Speed 15.0/15, Memory use 13.2/15 NVIDIA GeForce RTX 5090 Q8_0 18.3 GB / 32.0 GB 80.3 tok/s Runs well

Check it on your own hardware

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.