Mistral

What Mistral is for

Mistral is a general-purpose open-weights model from Mistral AI, released in 2023-09 with a single 7B size and 8K of context, licensed Apache 2.0. The catalogue records its strength as legacy 7B chat, very low resource.

Sizes and memory

The 7B needs about 5 GB of weights at Q4_K_M. That is the whole range; there is no larger or smaller variant in this record. 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
7B	5 GB	8 GB	NVIDIA Jetson Orin Nano 8GB

Hardware that runs Mistral

Every one of the 19 cards with dedicated memory in our hardware guide holds at least one size of Mistral at Q4_K_M. The cheapest that does is the NVIDIA Jetson Orin Nano 8GB at about $199, running the 7B.

The largest size any of them holds is the 7B at 5 GB, on the NVIDIA Jetson Orin Nano 8GB (8 GB at 68 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 3 recorded uses for Mistral: chat, edge, low-resource. It is tagged chat, instruction, lightweight.

Where Mistral sits in the Mistral family

Our catalogue holds 4 Mistral entries. Ordered by release date, Mistral (2023-09) is the 1st of them, and Mistral Small (2025-03) is newer.

The Apache 2.0 licence puts no usage ceiling on it, which matters if the thing you are building has users. That is the practical difference between this and a vendor community licence.

Context window: 8K

8K tokens comes to roughly 6,000 words at the usual three-quarters-of-a-word-per-token ratio: a long chat, a single source file, one chapter. It is the band where a retrieval step stops being optional, because the interesting documents do not fit. The recommended VRAM figure above assumes a working context rather than the maximum, and filling this one will push past it.

How to run Mistral 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 Mistral 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 "Mistral 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.