> This page is for OpenFold2, version 2.5.0.
> 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.

## Release 2.5.0

### Summary

This release extends GPU support with NVIDIA B300 and GB300, and removes the per-GPU engine-build step that was required by earlier releases. OpenFold2 now starts and serves requests with a single, simplified runtime configuration.

### Key Features

* **NVIDIA B300**: Added support for NVIDIA B300 GPU (288GB HBM3e)
* **NVIDIA GB300**: Added support for NVIDIA GB300 Grace Blackwell Ultra superchip (288GB HBM3e)
* **Confidence outputs (pLDDT, PAE, pTM)**: Each predicted structure in `structures_in_ranked_order` now includes per-residue `plddt`, the `predicted_aligned_error` matrix and its `max_predicted_aligned_error` bin upper bound, `aligned_confidence_probs`, and the `ptm_score` confidence scalar. Refer to [Response Fields](/nim/bionemo/openfold2/2.5.0/example-requests#response-fields) in Example Requests and the [API Reference](https://docs.nvidia.com/nim/bionemo/openfold2/2.5.0/api-reference/nim-api-post-call-monomer-structure-from-msa-and-template-biology-openfold-openfold-2-predict-structure-from-msa-and-template-post) for field-by-field details.
* **New optimized inference backend**: The NIM now runs on a fully NVIDIA-tuned inference path, delivering faster and more consistent predictions out of the box.

#### Backend Optimizations

The new inference backend introduces custom GPU kernels and graph-level optimizations:

* **New kernels**: Fused Adaptive Layer Norm; DualGemm (sm80, two variants); gated sigmoid (sm80); FlashAttention v2/v3 for Triangle Attention and Attention Pair Bias.
* **Pre-computed masks and buffers**: Pre-computed, left-aligned attention masks feed directly into the new dual-GEMM and attention kernels, with pre-computed buffers for the Pairformer and Diffusion Transformer (DiT) stacks.