> This page is for RFdiffusion, version 2.2.0.
> For other versions, use one of these documentation indexes:
> - 2.3.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/rfdiffusion/2.3.0/llms.txt
> - 2.2.0: https://docs.nvidia.com/nim/bionemo/rfdiffusion/2.2.0/llms.txt
> - 2.0.0: https://docs.nvidia.com/nim/bionemo/rfdiffusion/2.0.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/rfdiffusion/1.0.0/llms.txt

> For clean Markdown of any page, append .md to the page URL.
> For a complete documentation index, see https://docs.nvidia.com/nim/bionemo/llms.txt.
> For AI client integration (Claude Code, Cursor, etc.), connect to the MCP server at https://docs.nvidia.com/nim/bionemo/_mcp/server.

# Support Matrix

## Supported Hardware


### Minimum System Hardware Requirements

| CPU Cores    | RAM (GB) | NVMe SSD Storage (GB) | GPU Count |
|--------|-----------------|-----------|-------|
| 4  | 16              | 15       | 1 GPU from the list of Supported NVIDIA GPUs |

<Note>
The NVMe SSD Storage requirement of 15GB accounts for the RFdiffusion NIM image size plus additional space for Docker layer caching and temporary files during runtime.
</Note>

### Supported NVIDIA GPUs

The RFdiffusion NIM requires NVIDIA GPUs with at least 12 GB of GPU Memory. The RFdiffusion NIM is configured to run on a single GPU and should run on any NVIDIA GPU that meets this minimum memory requirement and has compute capability >7.0. The following configurations have been specifically tested for optimal performance and stability. We recommend using one of the following tested configurations:

| GPU    | GPU Memory (GB) | Microarchitecture | Precision | GPU Count | TensorRT Optimized |
|--------|-----------------|-------------------|-----------|-----------|-------------------|
| H100   | 80              | Hopper            | FP32/FP16 |  1        | ✓                 |
| A100   | 40-80           | Ampere            | FP32/FP16 |  1        | ✓                 |
| L40S   | 48              | Ada Lovelace      | FP32/FP16 |  1        | ✓                 |
| A10G   | 24              | Ampere            | FP32/FP16 |  1        | ✓                 |

<Note>
Starting with the 2.0 release, RFdiffusion NIM is optimized using [NVIDIA Warp](https://developer.nvidia.com/warp-python) and [NVIDIA TensorRT](https://developer.nvidia.com/tensorrt) frameworks, providing up to 2x speedup. Pre-compiled TensorRT engines are included for A100, A10G, L40S, and H100 GPUs. When running on other GPUs with compute capability >7.0, TensorRT engines will be built at runtime.
</Note>

## Testing Locally Available Hardware

* Begin with [Docker-supported operating system](https://docs.docker.com/engine/install/#supported-platforms)
* Install [Docker](https://docs.docker.com/engine/install/) - minimum version: 23.0.1
* Install [NVIDIA Drivers](https://www.nvidia.com/en-us/drivers/) - minimum version: 535
* Install the [NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html#installing-the-nvidia-container-toolkit) - minimum version: 1.13.5
* Verify your container runtime supports NVIDIA GPUs by running

```bash
docker run --rm --runtime=nvidia --gpus 1 ubuntu nvidia-smi
```

Example output:

```bash
+-----------------------------------------------------------------------------+
| NVIDIA-SMI 550.144.03   Driver Version: 550.144.03   CUDA Version: 12.4     |
|-------------------------------+----------------------+----------------------+
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|                               |                      |               MIG M. |
|===============================+======================+======================|
|   0  NVIDIA GeForce ...  Off  | 00000000:01:00.0 Off |                  N/A |
| 41%   30C    P8     1W / 260W |   2244MiB / 11264MiB |      0%      Default |
|                               |                      |                  N/A |
+-------------------------------+----------------------+----------------------+

+-----------------------------------------------------------------------------+
| Processes:                                                                  |
|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |
|        ID   ID                                                   Usage      |
|=============================================================================|
+-----------------------------------------------------------------------------+
```