> This page is for OpenFold2, version 1.0.0.
> For other versions, use one of these documentation indexes:
> - 2.6.0 (Latest) (default): https://docs.nvidia.com/nim/bionemo/openfold2/2.6.0/llms.txt
> - 2.5.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.5.0/llms.txt
> - 2.4.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.4.0/llms.txt
> - 2.3.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.3.0/llms.txt
> - 2.2.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.2.0/llms.txt
> - 2.1.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.1.0/llms.txt
> - 2.0.0: https://docs.nvidia.com/nim/bionemo/openfold2/2.0.0/llms.txt
> - 1.2.0: https://docs.nvidia.com/nim/bionemo/openfold2/1.2.0/llms.txt
> - 1.1.0: https://docs.nvidia.com/nim/bionemo/openfold2/1.1.0/llms.txt
> - 1.0.0: https://docs.nvidia.com/nim/bionemo/openfold2/1.0.0/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.

# Performance

Performance will also vary significantly depending on:

- The type of NVIDIA GPUs that are attached and available to the NIM
- The CPU type
- System RAM available

The following section details some performance expectations and provides general tips. These are not meant to be indicative of expected performance
 and performance on your system will vary from these values.

## Recommended System Requirements

 - Refer to [Supported Hardware](/nim/bionemo/openfold2/1.0.0/support-matrix) for requirements.

## Performance Benchmarks

The following are performance benchmarks for OpenFold2.

- Structure prediction performance is mostly dependent on GPU capability and memory. If you find structure prediction to be a bottleneck, consider using a higher memory device.

- The time required for structure prediction grows with sequence length.
- The time required for structure prediction grows with the total number of
sequences in the the alignements.
- Below are benchmark times, measured for each input chain, in sequential execution on a single 'NVIDIA H100 80GB HBM3'.
- The average value of LDDT-CA, for these protein chains, averaged over
2 runs, is 0.86.

| protein chain id |  7WBN_A  |  7ONG_A  |  7ZHT_A |7Y4I_A    |
|------------------|----------|----------|---------|----------|
|  sequence length |    98    |  304     |  562    |   914    |
|       time*      |   5.31   |  10.2    | 19.6    |  41.2    |

 *time to load parameter sets, compute features, and do forward pass,
averaged over the 5 models [1, 2, 3, 4, 5]


| algo feature / parameter             |   setting    |
---------------------------------------|--------------|
|      use_templates                   |    False     |
|     selected_models                  | [1,2,3,4,5]  |
|    relax_prediction                  |    False     |
| deepspeed evoformer kernel           |    active    |
| precision for deepspeed evoformer kernel    |    bf16      |
| precision for the rest of the model  |    fp32      |