---
title: "Using Chutes with GenLayer Validators"
description: "Chutes for GenLayer validators: API setup, model inference, authentication, discounts, and GPU-free AI workloads."
source: https://docs.genlayer.com/partners/chutes
last_updated: 2026-06-11
---

# Using Chutes with GenLayer Validators

Chutes is a serverless inference platform for open-source AI models that GenLayer validators can access through the Chutes API. Chutes runs on decentralized GPU infrastructure powered by Bittensor, so validators can call AI models through a simple HTTP API without running their own GPU infrastructure.

This guide explains how GenLayer validators can use Chutes to access AI models, authenticate requests, and run model inference from a validator environment. GenLayer Validators can request a discount on Chutes models by [filling out this form](https://docs.google.com/forms/d/e/1FAIpQLSd6I_Q0aiCalDQLX9FvzQ3hfFaKR4j1jaDEoa-jfpXhLclmog/viewform).

---

## Why Chutes

Running large AI models locally requires significant GPU infrastructure.

Chutes allows validators to run AI workloads without managing GPUs, by providing:

- Serverless model inference
- Decentralized GPU compute
- A simple REST API
- Access to many open-source AI models

Validators can integrate Chutes into their systems to run AI workloads while keeping infrastructure lightweight.

---

## Resources

- [Chutes Website](https://chutes.ai)
- [Chutes Documentation](https://chutes.ai/docs)
- [Chutes API Reference](https://api.chutes.ai/docs)
- [Available Models (API)](https://llm.chutes.ai/v1/models)
- [Model Browser (Web UI)](https://chutes.ai/app?type=llm)

---

## 1. Create a Chutes Account

First create an account: [https://chutes.ai](https://chutes.ai)

Chutes supports multiple login methods including:
- Google login
- Fingerprint-based account identity

When creating your account, store your fingerprint securely. It is an important identifier tied to your account.

---

## 2. Generate an API Key

After creating an account, generate an API key.

API keys allow applications to authenticate requests to the Chutes API.

### Steps:
1. Log into your Chutes account
2. Open account settings
3. Navigate to API Keys
4. Generate a new key
5. Store the key securely

Your API key will be used in authenticated API requests.

---

## 3. Authenticate Requests

The Chutes API uses Bearer token authentication.

Include your API key as a Bearer token in the `Authorization` header. You can verify your key is working by listing the available models:

```bash
curl https://llm.chutes.ai/v1/models \
  -H "Authorization: Bearer $CHUTES_API_KEY"
```

The API follows standard REST conventions and returns JSON responses.

---

## 4. Run Your First Model from a Validator

Validators can call the Chutes inference API directly from their validator environment.

Chutes provides an OpenAI-compatible inference endpoint for running language models.

**Endpoint:** `https://llm.chutes.ai/v1/chat/completions`

This naming convention is the same format commonly used on Hugging Face, where models are referenced as: `organization/model`

For example: `deepseek-ai/DeepSeek-R1`

The list of available models changes frequently. You can retrieve the latest models here:
- API endpoint: [https://llm.chutes.ai/v1/models](https://llm.chutes.ai/v1/models)
- Browse them visually: [https://chutes.ai/app?type=llm](https://chutes.ai/app?type=llm)

### Example request from a validator environment:

```bash
curl https://llm.chutes.ai/v1/chat/completions \
  -H "Authorization: Bearer $CHUTES_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "model_team/model_name",
    "messages": [
      {
        "role": "user",
        "content": "Explain decentralized AI infrastructure in one sentence."
      }
    ]
  }'
```

### Example response:

```json
{
  "choices": [
    {
      "message": {
        "role": "assistant",
        "content": "Decentralized AI infrastructure allows developers and validators to access distributed compute resources for running AI models without owning the underlying hardware."
      }
    }
  ]
}
```

Validators can then use the returned output inside their application logic.

**Example use cases include:**
- Evaluating prompts or responses
- Generating summaries or structured outputs
- Integrating AI into validator tooling

Because the Chutes API is stateless and accessed via HTTPS, it can be integrated into validator environments without additional infrastructure.

---

## 5. Discover Available Models

The list of models available on Chutes changes frequently.

You can retrieve the current list using: [https://llm.chutes.ai/v1/models](https://llm.chutes.ai/v1/models)

**Example:**

```bash
curl https://llm.chutes.ai/v1/models
```

This endpoint returns the latest models available for inference.

Models can also be browsed in the Chutes web interface: [https://chutes.ai/app?type=llm](https://chutes.ai/app?type=llm)

---

## Best Practices

For validator deployments:
- Create a dedicated API key
- Store the key in environment variables
- Avoid committing API keys to source control

**Example:**

```bash
export CHUTES_API_KEY=your_api_key_here
```

---

## Next Steps

- [Explore the full API reference](https://api.chutes.ai/docs)
- [View the latest available models](https://llm.chutes.ai/v1/models)
- [Browse models in the Chutes app](https://chutes.ai/app?type=llm)
- [Learn more about Chutes](https://chutes.ai/docs)