Biomolecular structure prediction now runs on proteome‑scale worklists. The bottleneck is no longer model accuracy but the speed at which thousands of independent targets move through parsing, featurization, GPU inference and output writing. NVIDIA’s BioNeMo Inference Runtime (BioIR) removes this bottleneck by accelerating the core inference stack while keeping a familiar PyTorch workflow.
BioIR works in three layers. First, kernel selection picks the fastest implementation—custom BioIR kernels, cuEquivariance or PyTorch fallback—based on the model, GPU, datatype and tensor shape. Second, module optimization captures CUDA Graphs for compatible modules, eliminating launch overhead on repeated forward passes. Third, pipeline scaling uses a Ray executor that places one full model replica on each visible GPU; CPU stages (parsing, tokenization, feature generation, writing) run in parallel with GPU folding, so the GPU never waits for data.
To use BioIR you need Python 3.12+, a supported NVIDIA GPU and driver, a staged model checkpoint and per‑chain A3M MSAs. No CUDA toolkit, nvcc or CMake is required. The library ships as a wheel with pre‑compiled CUBINs and can be invoked either through an end‑to‑end Processor that handles an InputRequest from start to PDB/mmCIF output, or via direct PyTorch integration for custom pipelines.
In a matched 8×H100 benchmark with 1,000 human dimer targets, BioIR‑accelerated Boltz‑2 folded 58.5K residues per GPU‑hour versus 20.2K for the open‑source torch.compile baseline—a 2.90× throughput gain. Extrapolated to one million targets, this translates to an estimated energy saving of roughly 23 MWh (11 MWh vs 35 MWh at TDP equivalents). BioIR has already powered the recent AlphaFold Database expansion, generating ~31 million candidate complexes across 4,777 proteomes.
Key takeaways: BioIR speeds Boltz‑2, OpenFold2 and OpenFold3 inference on NVIDIA GPUs while staying in plain PyTorch; it scales worklists via Ray replica mode without splitting a single forward pass across GPUs; and it delivers measurable throughput and energy improvements for proteome‑scale campaigns.
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