Ministral 3

What Ministral 3 is for

Ministral 3 takes images as well as text. Mistral AI shipped it in 2025-12 with 3 sizes (3B, 8B, 14B), 128K of context and a MRL License (open-weights) licence. Running a vision-language model locally costs more memory than a text model of the same parameter count, because the image encoder and the projected image tokens both occupy the context. The catalogue records its strength as edge deployment, small chat with vision.

Sizes and memory

The 3B needs about 2 GB of weights at Q4_K_M and the 14B about 9 GB, a spread of 7 GB across the family. The catalogue's default pick is 8B. Recommended VRAM adds roughly half again on top of the weights for the KV cache and the runtime.

Size	Weights at Q4_K_M	Recommended VRAM	Smallest card in our guide that holds it
3B	2 GB	3 GB	NVIDIA Jetson Orin Nano 8GB
8B	5 GB	8 GB	NVIDIA Jetson Orin Nano 8GB
14B	9 GB	14 GB	NVIDIA RTX 3060 12GB

Hardware that runs Ministral 3

Every one of the 19 cards with dedicated memory in our hardware guide holds at least one size of Ministral 3 at Q4_K_M. The cheapest that does is the NVIDIA Jetson Orin Nano 8GB at about $199, running the 8B.

The largest size any of them holds is the 14B at 9 GB, on the NVIDIA RTX 3060 12GB (12 GB at 360 GB/s). Bandwidth, not capacity, sets the speed once a size fits, so a card further down the list with faster memory will generate more tokens per second on the same weights than a larger, slower one.

What people use it for

The catalogue lists 5 recorded uses for Ministral 3: edge, vision, chat, tools, mobile. It is tagged chat, edge, vision, tools.

Where Ministral 3 sits in the Ministral family

Ministral 3 is the only Ministral entry in our catalogue, so there is no in-family alternative to weigh it against; the comparison to make is against the models listed at the foot of this page.

It ships under the MRL License (open-weights), which is neither a standard open-source licence nor a closed one. Read the terms before shipping anything commercial on top of it.

Context window: 128K

128K tokens is roughly 96,000 words at the usual ratio, which is a small repository or a whole book. Two cautions come with a window this size: the KV cache at full length can rival the weights for memory, and models rarely use the far end of their advertised context as well as they use the near end. Treat it as headroom, not as a promise.

How to run Ministral 3 locally

Our catalogue does not record registry tags, so look the current tag up in the Ollama library or on Hugging Face before pasting these. The commands below are the shape of the workflow, not a copy-and-paste recipe.

Option 1: Ollama (simplest)

# find the tag for Ministral 3 at ollama.com/library
ollama run <tag>

Option 2: Mullama (production)

mullama pull <tag>
mullama run <tag>

Option 3: llama.cpp (CLI)

# download a GGUF from Hugging Face, searching for "Ministral 3 GGUF"
./llama-cli -m model.Q4_K_M.gguf -p "Hello, AI!"

Option 4: Python with Mullama or llama-cpp-python

from mullama import Model, Context
model = Model.load("model.Q4_K_M.gguf", n_gpu_layers=99)
ctx = Context(model, n_ctx=4096)
print(ctx.generate("Hello, AI!", 256))

Sources

Model record from src/data/models.json (2026-06-29); card capacities from src/data/gpus.json (2026-06-29). Upstream: Curated from ollama.com/library + community benchmarks + paperswithcode + huggingface.