> ## 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.

# On-demand deployment scaling

> Understanding Fireworks.ai system scaling and request handling capabilities.

## System scaling

**Q: How does the system scale?**

Our system is **horizontally scalable**, meaning it:

* Scales linearly with additional **replicas** of the deployment
* **Automatically allocates resources** based on demand
* Manages **distributed load handling** efficiently

***

## Auto scaling

**Q: Do you support Auto Scaling?**

Yes, our system supports **auto scaling** with the following features:

* **Scaling down to zero** capability for resource efficiency
* Controllable **scale-up and scale-down velocity**
* **Custom scaling rules and thresholds** to match your specific needs

***

## Throughput capacity

**Q: What’s the supported throughput?**

Throughput capacity typically depends on several factors:

* **Deployment type** (serverless or on-demand)
* **Traffic patterns** and **request patterns**
* **Hardware configuration**
* **Model size and complexity**

***

## Request handling

**Q: What factors affect the number of simultaneous requests that can be handled?**

The request handling capacity is influenced by multiple factors:

* **Model size and type**
* **Number of GPUs** allocated to the deployment
* **GPU type** (e.g., A100 vs. H100)
* **Prompt size** and **generation token length**
* **Deployment type** (serverless vs. on-demand)

***

## Additional resources

* **Discord Community**: [discord.gg/fireworks-ai](https://discord.gg/fireworks-ai)
* **Email Support**: [inquiries@fireworks.ai](mailto:inquiries@fireworks.ai)
* **Documentation**: [Fireworks.ai docs](https://fireworks.ai/docs)