Deploy a Hugging Face Model

Deploy any supported Hugging Face model on Inference.net without managing your own infrastructure. This guide walks through registering a model, creating a deployment, and verifying the endpoint.

What you'll have when you finish

• one dedicated deployment serving a Hugging Face model
• a public model identifier you can use with the standard API shape
• a verified smoke test confirming the model is responding

Before you start

• an Inference.net account with deployment access
• the Hugging Face model ID you want to deploy (e.g., meta-llama/Llama-3.2-1B-Instruct)

Step 1: Register the model

Hugging Face models are served as custom models. Register the one you want first — you don't paste a model ID into the deployment flow directly.

In the dashboard, open Models, switch to the My Models tab, and click Create new Model. Choose the Hugging Face path and provide:

• the Hugging Face model ID
• a Hugging Face token if the model is a gated (private) repo
• the GPUs it can run on — 1–2 H100s or H200s
• the engine — vLLM or SGLang

Model registration is in beta; reach out to the team to have it enabled for your account before you start.

See Deploy a Hugging Face Model for the full registration walkthrough.

Step 2: Create the deployment

Once the model is registered, follow the same deployment creation flow described in Deploy a Trained Model. The key difference is that you select your registered Hugging Face model from the deployment picker.

Step 3: Verify the endpoint

Send a smoke test request using the deployment's public model identifier:

curl -N https://api.inference.net/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $INFERENCE_API_KEY" \
  -d '{
    "messages": [
      { "role": "user", "content": "Hello, world!" }
    ],
    "model": "your-team/your-deployment-id",
    "stream": true
  }'

Next steps