Mistral Devstral Small

Deploy Mistral Devstral Small model on Koyeb’s high-performance cloud infrastructure.

With one click, get a dedicated GPU-powered inference endpoint ready to handle requests with built-in autoscaling and scale-to-zero.

Overview of Mistral Devstral Small

Devstral, Mistral's latest SOTA model with 24 billion parameters, is finetuned from Mistral Small 3.1. It excels in code generation, solving complex tasks, and powering software engineering agents. Ideal for exploring codebases, editing multiple files, and building coding agents.

Devstral will be served with the vLLM inference engine, optimized for high-throughput and low-latency model serving.

The default GPU for running this model is the Nvidia A100 instance type. You are free to adjust the GPU instance type to fit your workload requirements.

Quickstart

Mistral Devstral Small one-click model is served using the vLLM engine. vLLM is an advanced inference engine designed for high-throughput and low-latency model serving. Optimized for large language models, it provides efficient performance and compatibility with the OpenAI API.

After you deploy the Devstral model, copy the Koyeb App public URL similar to https://<YOUR_DOMAIN_PREFIX>.koyeb.app and create a simple Python file with the following content to start interacting with the model.

import os

from openai import OpenAI

client = OpenAI(
  api_key = os.environ.get("OPENAI_API_KEY", "fake"),
  base_url="https://<YOUR_DOMAIN_PREFIX>.koyeb.app/v1",
)

chat_completion = client.chat.completions.create(
    messages=[
        {
            "role": "user",
            "content": "Find the bug in my code.",
        }
    ],
    model="mistralai/Devstral-Small-2505",
    max_tokens=30,
)

print(chat_completion.to_json(indent=4))

The snippet above is using the OpenAI SDK to interact with the Mistral Devstral Small model thanks to vLLM OpenAI compatibility.

Take care to replace the base_url value in the snippet with your Koyeb App public URL.

Executing the Python script will return the model's response to the input message.

python main.py

{
    "id": "chatcmpl-4edefc61-0d54-9c6d-a123-71068f70b24f",
    "choices": [
        {
            "finish_reason": "length",
            "index": 0,
            "logprobs": null,
            "message": {
                "content": "Sure, I'd be happy to help you find the bug in your code! Please provide the code you are working on, and describe any issues or",
                "role": "assistant",
                "tool_calls": [],
                "reasoning_content": null
            },
            "stop_reason": null
        }
    ],
    "created": 1748012427,
    "model": "mistralai/Devstral-Small-2505",
    "object": "chat.completion",
    "usage": {
        "completion_tokens": 30,
        "prompt_tokens": 10,
        "total_tokens": 40,
        "prompt_tokens_details": null
    },
    "prompt_logprobs": null
}

Securing the Inference Endpoint

To ensure that only authenticated requests are processed, we recommend setting up an API key to secure your inference endpoint. Follow these steps to configure the API key:

1. Generate a strong unique API key to use for authentication
2. Navigate to your Koyeb Service settings
3. Add a new environment variable named VLLM_API_KEY and set its value to your secret API key
4. Save the changes and redeploy to update the service

Once the service is updated, all requests to the inference endpoint will require the API key.

When making requests, ensure the API key is included in the headers. If you are using the OpenAI SDK, you can provide the API key through the api_key parameter when instantiating the OpenAI client. Alternatively, you can set the API key using the OPENAI_API_KEY environment variable. For example:

OPENAI_API_KEY=<YOUR_API_KEY> python main.py