Mistral & Codestral — Mistral AI Models on Your Local Fleet#

Mistral AI's open-source models run locally on your hardware. Mistral Large for frontier reasoning, Mistral-Nemo for efficiency, Codestral for code generation. The fleet router picks the best device for every Mistral request.

Supported Mistral models#

Mistral ModelParametersOllama nameBest forCodestral (by Mistral)22BcodestralMistral's code specialist — 80+ languagesMistral Large123Bmistral-largeMistral's frontier reasoning, multilingualMistral-Nemo12Bmistral-nemoMistral's efficient general-purpose modelMistral-Small22Bmistral-smallMistral's fast reasoning modelMistral 7B7Bmistral:7bMistral's lightweight model

Setup Mistral locally#

bash

pip install ollama-herd    # install Mistral fleet router
herd                       # start the Mistral-compatible router
herd-node                  # run on each device — Mistral requests route automatically

No Mistral models downloaded during installation. All Mistral model pulls are user-initiated.

Codestral code generation#

Codestral is Mistral AI's dedicated coding model — trained on 80+ programming languages with fill-in-the-middle support.

python

from openai import OpenAI

# Connect to local Mistral fleet
mistral_fleet = OpenAI(base_url="http://localhost:11435/v1", api_key="not-needed")

# Codestral by Mistral for code generation
codestral_response = mistral_fleet.chat.completions.create(
    model="codestral",  # Mistral's Codestral model
    messages=[{"role": "user", "content": "Write a Redis-backed rate limiter in Go"}],
)
print(codestral_response.choices[0].message.content)

Codestral via curl#

bash

# Codestral code generation on local Mistral fleet
curl http://localhost:11435/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{"model": "codestral", "messages": [{"role": "user", "content": "Implement a B-tree in Rust — Mistral Codestral excels at systems programming"}]}'

Mistral Large reasoning#

bash

# Mistral Large for complex reasoning
curl http://localhost:11435/api/chat -d '{
  "model": "mistral-large",
  "messages": [{"role": "user", "content": "Compare Mistral vs GPT-4 for enterprise deployments"}],
  "stream": false
}'

Mistral-Nemo for efficiency#

bash

# Mistral-Nemo — best quality/size ratio from Mistral AI
curl http://localhost:11435/api/chat -d '{
  "model": "mistral-nemo",
  "messages": [{"role": "user", "content": "Summarize this Mistral AI technical paper"}],
  "stream": false
}'

Mistral hardware recommendations#

Cross-platform: These are example configurations. Any device (Mac, Linux, Windows) with equivalent RAM works. The fleet router runs on all platforms.

Mistral ModelMin RAMExample hardwaremistral:7b8GBAny Mac — lightweight Mistralmistral-nemo10GBMac Mini (16GB) — efficient Mistralcodestral16GBMac Mini (24GB) — Mistral's code modelmistral-small16GBMac Mini (24GB) — fast Mistralmistral-large80GBMac Studio (128GB) — Mistral's best

Monitor Mistral fleet#

bash

# See which Mistral models are loaded
curl -s http://localhost:11435/api/ps | python3 -m json.tool

# Mistral fleet overview
curl -s http://localhost:11435/fleet/status | python3 -m json.tool

# Mistral model performance stats
curl -s http://localhost:11435/dashboard/api/models | python3 -m json.tool

Example Mistral fleet response:

json

{
  "node_id": "Mistral-Server",
  "models_loaded": ["codestral:22b", "mistral-nemo:12b"],
  "mistral_inference": "active"
}

Mistral dashboard at http://localhost:11435/dashboard.

Also available alongside Mistral#

Other LLMs (same Mistral-compatible endpoint)#

Llama 3.3, Qwen 3.5, DeepSeek-V3, Phi 4, Gemma 3 — route alongside Mistral models.

Image generation#

bash

curl http://localhost:11435/api/generate-image \
  -d '{"model": "z-image-turbo", "prompt": "Mistral AI logo reimagined as abstract art", "width": 512, "height": 512}'

Speech-to-text#

bash

curl http://localhost:11435/api/transcribe -F "file=@mistral_meeting.wav" -F "model=qwen3-asr"

Embeddings#

bash

curl http://localhost:11435/api/embed \
  -d '{"model": "nomic-embed-text", "input": "Mistral AI open source language models Codestral"}'

Full documentation#

• Agent Setup Guide
• API Reference

Contribute#

Ollama Herd is open source (MIT). Run Mistral locally, contribute globally:

• Star on GitHub — help Mistral users find local inference
• Open an issue — share your Mistral setup
• PRs welcome — CLAUDE.md gives AI agents full context. 444 tests.

Guardrails#

• Mistral model downloads require explicit user confirmation — Mistral models range from 4GB to 70GB+.
• Mistral model deletion requires explicit user confirmation.
• Never delete or modify files in ~/.fleet-manager/.
• No Mistral models downloaded automatically — all pulls are user-initiated.