Deployments Quickstart ↗

fireworks tutorial beginner models deployment
no
Summary: Deploy models on dedicated GPUs in minutes

Original Documentation

Documentation Index#

Fetch the complete documentation index at: https://docs.fireworks.ai/llms.txt Use this file to discover all available pages before exploring further.

Deploy models on dedicated GPUs in minutes

On-demand deployments are dedicated GPUs that give you better performance, no rate limits, fast autoscaling, and a wider selection of models than serverless. This quickstart will help you spin up your first on-demand deployment in minutes.

Step 1: Create and export an API key#

Before you begin, create an API key in the Fireworks dashboard. Click Create API key and store it in a safe location.

Once you have your API key, export it as an environment variable in your terminal:

    export FIREWORKS_API_KEY="your_api_key_here"
    ```
  <span class="tab-end"></span>

  <span class="tab-start" data-tab-title="Windows"></span>
```powershell
    setx FIREWORKS_API_KEY "your_api_key_here"
    ```
  <span class="tab-end"></span>
<span class="tab-group-end"></span>

## Step 2: Install the CLI

To create and manage on-demand deployments, you'll need the `firectl` CLI tool. Install it using one of the following methods, based on your platform:


  ```bash
  brew tap fw-ai/firectl
  brew install firectl

  # If you encounter a failed SHA256 check, try first running
  brew update
curl https://storage.googleapis.com/fireworks-public/firectl/stable/darwin-arm64.gz -o firectl.gz
gzip -d firectl.gz && chmod a+x firectl
sudo mv firectl /usr/local/bin/firectl
sudo chown root: /usr/local/bin/firectl
curl https://storage.googleapis.com/fireworks-public/firectl/stable/darwin-amd64.gz -o firectl.gz
gzip -d firectl.gz && chmod a+x firectl
sudo mv firectl /usr/local/bin/firectl
sudo chown root: /usr/local/bin/firectl
wget -O firectl.gz https://storage.googleapis.com/fireworks-public/firectl/stable/linux-amd64.gz
gunzip firectl.gz
sudo install -o root -g root -m 0755 firectl /usr/local/bin/firectl
wget -L https://storage.googleapis.com/fireworks-public/firectl/stable/firectl.exe

Then, sign in:

firectl signin

Step 3: Create a deployment#

This command will create a deployment of GPT OSS 120B optimized for speed. It will take a few minutes to complete. The resulting deployment will scale up to 1 replica.

firectl deployment create accounts/fireworks/models/gpt-oss-120b \
        --deployment-shape fast \
        --scale-down-window 5m \
        --scale-up-window 30s \
        --min-replica-count 0 \
        --max-replica-count 1 \
        --scale-to-zero-window 5m \
        --wait

fast is called a deployment shape, which is a pre-configured deployment template created by the Fireworks team that sets sensible defaults for most deployment options (such as hardware type).

You can also pass throughput or cost to --deployment-shape:

  • throughput creates a deployment that trades off latency for lower cost-per-token at scale
  • cost creates a deployment that trades off latency and throughput for lowest cost-per-token at small scale, usually for early experimentation and prototyping

While we recommend using a deployment shape, you are also free to pass your own configuration to the deployment via our deployment guide.

The response will look like this:

Name: accounts/<YOUR ACCOUNT ID>/deployments/<DEPLOYMENT ID>
Create Time: <CREATION_TIME>
Expire Time: <EXPIRATION_TIME>
Created By: <YOUR EMAIL>
State: CREATING
Status: OK
Min Replica Count: 0
Max Replica Count: 1
Desired Replica Count: 0
Replica Count: 0
Autoscaling Policy:
  Scale Up Window: 30s
  Scale Down Window: 5m0s
  Scale To Zero Window: 5m0s
Base Model: accounts/fireworks/models/gpt-oss-120b
...other fields...

Take note of the Name: field in the response, as it will be used in the next step to query your deployment.

Learn more about deployment options→

Learn more about autoscaling options→

Step 4: Query your deployment#

Now you can query your on-demand deployment using the same API as serverless models, but using your dedicated deployment. Replace <DEPLOYMENT_NAME> in the below snippets with the value from the Name: field in the previous step:

Install the Fireworks Python SDK:

The SDK is currently in alpha. Use the --pre flag when installing to get the latest version. 

    pip install --pre fireworks-ai
    ```

```bash
    poetry add --pre fireworks-ai
    ```

```bash
    uv add --pre fireworks-ai
    ```


Then make your first on-demand API call:

```python
  from fireworks import Fireworks

  client = Fireworks()

  response = client.chat.completions.create(
      model="accounts/fireworks/models/gpt-oss-120b#<DEPLOYMENT_NAME>",
      messages=[{
          "role": "user",
          "content": "Explain quantum computing in simple terms",
      }],
  )

  print(response.choices[0].message.content)
  ```
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<span class="tab-start" data-tab-title="Python (OpenAI SDK)"></span>
```python
  import os
  from openai import OpenAI

  client = OpenAI(
      api_key=os.environ.get("FIREWORKS_API_KEY"),
      base_url="https://api.fireworks.ai/inference/v1"
  )

  response = client.chat.completions.create(
      model="<DEPLOYMENT_NAME>",
      messages=[{
          "role": "user",
          "content": "Explain quantum computing in simple terms",
      }],
  )

  print(response.choices[0].message.content)
  ```
<span class="tab-end"></span>

<span class="tab-start" data-tab-title="JavaScript"></span>
```javascript
  import OpenAI from "openai";

  const client = new OpenAI({
    apiKey: process.env.FIREWORKS_API_KEY,
    baseURL: "https://api.fireworks.ai/inference/v1",
  });

  const response = await client.chat.completions.create({
    model: "<DEPLOYMENT_NAME>",
    messages: [
      {
        role: "user",
        content: "Explain quantum computing in simple terms",
      },
    ],
  });

  console.log(response.choices[0].message.content);
  ```
<span class="tab-end"></span>

<span class="tab-start" data-tab-title="curl"></span>
```bash
  curl https://api.fireworks.ai/inference/v1/chat/completions \
    -H "Content-Type: application/json" \
    -H "Authorization: Bearer $FIREWORKS_API_KEY" \
    -d '{
      "model": "<DEPLOYMENT_NAME>",
      "messages": [
        {
          "role": "user",
          "content": "Explain quantum computing in simple terms"
        }
      ]
    }'
  ```
<span class="tab-end"></span>
<span class="tab-group-end"></span>

The examples from the Serverless quickstart will work with this deployment as well, just replace the model string with the deployment-specific model string from above.

[Serverless quickstart→](/getting-started/quickstart)

## Common use cases

### Autoscale based on requests per second

```bash
firectl deployment create accounts/fireworks/models/gpt-oss-120b \
      --deployment-shape fast \
      --scale-down-window 5m \
      --scale-up-window 30s \
      --scale-to-zero-window 5m \
      --min-replica-count 0 \
      --max-replica-count 4 \
      --load-targets requests_per_second=5 \
      --wait

Autoscale based on concurrent requests#

firectl deployment create accounts/fireworks/models/gpt-oss-120b \
        --deployment-shape fast \
        --scale-down-window 5m \
        --scale-up-window 30s \
        --scale-to-zero-window 5m \
        --min-replica-count 0 \
        --max-replica-count 4 \
        --load-targets concurrent_requests=5 \
        --wait

Next steps#

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Complete API documentation

Link last verified June 7, 2026. View original ↗
Source: Fireworks AI Docs
Link last verified: 2026-06-07