Multimodal

Ministral 3 14B Instruct

Mistral AI's powerful 14 billion parameter instruction-tuned model

Command to Run on Jetson Model Details

Parameters 14B

Modalities

Text

Context Length 262K

License Apache 2.0

Precision

FP8

Serve the model

Start server

Choose module, then engine and optional parameters on the left, then copy the serve command by clicking the button on the right.

Command

Call the model over Web API

Copy a client command below and paste it into your terminal to make a Web API request to the model you just served.

curl -s http://${JETSON_HOST}:8000/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistralai/Ministral-3-14B-Instruct-2512",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

from openai import OpenAI

client = OpenAI(
    base_url="http://${JETSON_HOST}:8000/v1",
    api_key="not-needed",  # vLLM / llama.cpp typically do not enforce a key
)

completion = client.chat.completions.create(
    model="mistralai/Ministral-3-14B-Instruct-2512",
    messages=[{"role": "user", "content": "Hello!"}],
)
print(completion.choices[0].message.content)

With ollama serve on the Jetson, call from another host (set ${JETSON_HOST} or use the field). Match the model name to what you pulled on device.

curl -s http://${JETSON_HOST}:11434/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "Ministral-3-14B-Instruct-2512",
    "messages": [{"role": "user", "content": "Why is the sky blue?"}]
  }'

With ollama serve on the Jetson, call from another host (set ${JETSON_HOST} or use the field). Match the model name to what you pulled on device.

curl -s http://${JETSON_HOST}:11434/api/generate -d '{
  "model": "Ministral-3-14B-Instruct-2512",
  "prompt": "Why is the sky blue?",
  "stream": false
}'

from openai import OpenAI

client = OpenAI(
    base_url="http://${JETSON_HOST}:11434/v1",
    api_key="ollama",  # required by the client; Ollama ignores it
)

completion = client.chat.completions.create(
    model="Ministral-3-14B-Instruct-2512",
    messages=[{"role": "user", "content": "Why is the sky blue?"}],
)
print(completion.choices[0].message.content)

import json
import urllib.request

url = "http://${JETSON_HOST}:11434/api/generate"
payload = json.dumps(
    {
        "model": "Ministral-3-14B-Instruct-2512",
        "prompt": "Why is the sky blue?",
        "stream": False,
    }
).encode("utf-8")
req = urllib.request.Request(
    url,
    data=payload,
    headers={"Content-Type": "application/json"},
)
with urllib.request.urlopen(req) as resp:
    body = json.load(resp)
    print(body.get("response", body))

One-shot inference

Choose a Jetson module, adjust optional parameters, then copy the command to run a single inference on the device.

Command

·Shell

Model Details

View on HuggingFace

Mistral AI’s Ministral 3 14B Instruct is the most powerful instruction-tuned variant in the Ministral 3 family.

For Ollama, use the ministral-3:14b tag from the official library (check the library readme for minimum Ollama version).

The Ministral 3 Instruct model offers the following capabilities:

Vision: Enables the model to analyze images and provide insights based on visual content, in addition to text.
Multilingual: Supports dozens of languages, including English, French, Spanish, German, Italian, Portuguese, Dutch, Chinese, Japanese, Korean, Arabic.
System Prompt: Maintains strong adherence and support for system prompts.
Agentic: Offers best-in-class agentic capabilities with native function calling and JSON outputting.
Edge-Optimized: Delivers best-in-class performance at a small scale, deployable anywhere.
Apache 2.0 License: Open-source license allowing usage and modification for both commercial and non-commercial purposes.
Large Context Window: Supports a 256k context window.