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> - 1.4.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.4.0/llms.txt
> - 1.3.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.3.0/llms.txt
> - 1.2.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.2.0/llms.txt
> - 1.1.0: https://docs.nvidia.com/nim/bionemo/openfold3/1.1.0/llms.txt
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## Release 1.5.0

### Summary

This release adds support for NVIDIA B300 and GB300 GPUs and introduces a new, fully NVIDIA-optimized inference backend that replaces the previous hybrid implementation.

### Key Features

* **NVIDIA B300**: Added support for NVIDIA B300 GPU
* **NVIDIA GB300**: Added support for NVIDIA GB300 superchip
* **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.
* **Lower peak memory**: The confidence module is evaluated in a loop to reduce memory usage.
* **Diffusion Transformer**: Removed the bias computation for Attention Pair Bias, replacing it with three kernels (one reduce, one GEMM, one padding).
* **Optimized query-to-keys path** in the attention computation.

### Notes and Limitations

* The optional PyTorch-only inference mode from earlier releases is no longer available. All requests run on the new optimized backend.
* All features from release 1.4.0 remain supported.