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

# Automated Performance Testing

> Learn about the performance testing process in Vast.ai Serverless.

Vast Serverless relies on **benchmark testing** to determine the most cost-effective GPU when scaling up (which workers to recruit), routing requests (which workers have available capacity), and scaling down (which workers to release).

This benchmark is part of the **PyWorker configuration** within the SDK and is an integral component of how Vast Serverless operates.

## How Benchmark Testing Works

When a new Workergroup is created, the serverless engine enters a **learning phase**. During this phase, it recruits a variety of machine types from those specified in `search_params`. Each new worker runs the user-configured benchmark and evaluates performance, which are reported to the serverless engine.

As traffic scales up and down, the serverless engine builds an **application-specific understanding of cost vs. performance**, which it then uses to make informed decisions about future worker recruitment and release.

## Best Practices for Initial Scaling

The speed at which the serverless engine “settles” into the most cost-effective mix of workers can vary depending on how quickly workers are recruited and released. Because of this, it is recommended to apply a **test load during the first day of operation** to help the system efficiently explore and converge on optimal hardware choices.

Best practice is to scale to double the number of expected required workers, then scale back down, 3 separate times.

## Simulating Load

For examples of how to simulate load against your endpoint, see the client examples in the Vast SDK repository:

[https://github.com/vast-ai/vast-sdk/blob/main/examples/client/vllm\_load\_example.py](https://github.com/vast-ai/vast-sdk/blob/main/examples/client/vllm_load_example.py)

## Running the Benchmark Yourself

The same benchmark workload can be invoked on demand from the CLI against any list of GPU classes, before (or independent of) creating a Workergroup. See [Choosing GPUs for Your Workload](/guides/serverless/choosing-gpus) under **Instances → Find & rent** for the walkthrough.