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

# Using GPUs

GPUs accelerate computational workloads through parallel processing. Originally designed for graphics rendering, modern GPUs are essential for AI models, large-scale data processing, and other compute-intensive applications.

Cerebrium provides GPU access through configuration in the `cerebrium.toml` file, without requiring infrastructure management.

## Specifying GPUs

Configure GPUs in the `[cerebrium.hardware]` section of `cerebrium.toml`, specifying the type (`compute` parameter) and quantity (`gpu_count`). Additional deployment and scaling considerations are covered in the sections below.

## Available GPUs

The platform offers GPUs ranging from cost-effective development options to high-end enterprise hardware.

| GPU Model    | Identifier         | VRAM (GB) | Max GPUs | Plan required |
| ------------ | ------------------ | --------- | -------- | ------------- |
| NVIDIA B300  | BLACKWELL\_B300    | 262       | 8        | Enterprise    |
| NVIDIA B200  | BLACKWELL\_B200    | 180       | 8        | Enterprise    |
| NVIDIA H200  | HOPPER\_H200       | 141       | 8        | Enterprise    |
| NVIDIA H100  | HOPPER\_H100       | 80        | 8        | Enterprise    |
| RTX PRO 6000 | BLACKWELL\_RTX6000 | 96        | 8        | Standard      |
| NVIDIA A100  | AMPERE\_A100\_80GB | 80        | 8        | Standard      |
| NVIDIA A100  | AMPERE\_A100\_40GB | 40        | 8        | Standard      |
| NVIDIA L40s  | ADA\_L40           | 48        | 8        | Hobby+        |
| NVIDIA L4    | ADA\_L4            | 24        | 8        | Hobby+        |
| NVIDIA A10   | AMPERE\_A10        | 24        | 8        | Hobby+        |
| NVIDIA T4    | TURING\_T4         | 16        | 8        | Hobby+        |
| AWS Trainium | TRN1               | 32        | 8        | Hobby+        |

<Info>
  The identifier is used in the `cerebrium.toml` file. It consists of the GPU
  model generation and model name to avoid ambiguity.
</Info>

<Tip>
  GPU selection is also possible using the `--compute` and `--gpu-count` flags
  during application initialization.
</Tip>

## Multi-GPU Configuration

Multiple GPUs are configured in the `cerebrium.toml` file:

```toml theme={null}
[cerebrium.hardware]
compute = "AMPERE_A100_80GB"
gpu_count = 4        # Number of GPUs needed
cpu = 8
memory = 128.0
```

GPU availability varies by region and provider. Narrowing the provider and region constraints increases the likelihood of request queuing.
For guaranteed burst capacity, contact the [enterprise plan](mailto:sales@cerebrium.ai) team.