NVIDIA Details BioNeMo Inference Runtime (BioIR): 2.90x Higher Boltz-2 Folding Throughput and 58.5K Residues per GPU-Hour on 8xH100
NVIDIA released BioNeMo Inference Runtime (BioIR), an open-source PyTorch-compatible library delivering 2.90x higher Boltz-2 protein-folding throughput on 8xH100 GPUs.
NVIDIA detailed BioIR, a Python library that accelerates Boltz-2, OpenFold2, and OpenFold3 structure-prediction inference on NVIDIA GPUs while preserving standard PyTorch workflows. On a matched benchmark of 1,000 human dimers on 8xH100 80GB GPUs, BioIR delivered 58.5K folded residues per GPU-hour versus 20.2K for a torch.compile baseline, a 2.90x throughput gain. BioIR already powered the AlphaFold Database expansion, generating about 31 million candidate complexes across 4,777 proteomes, with 1.81 million released as high-confidence predictions. Extrapolated to 1 million targets, estimated folding energy drops from 35 MWh to 11 MWh at 8-GPU TDP equivalents.
- BioIR accelerates Boltz-2, OpenFold2, and OpenFold3 in plain PyTorch without nvcc, CUDA source, or toolkit.
- Three optimization layers: kernel selection, CUDA Graph module optimization, and Ray-based pipeline scaling.
- Matched benchmark shows 58.5K versus 20.2K folded residues per GPU-hour on 8xH100 GPUs.
- Already used at scale for AlphaFold Database expansion: 31M candidate complexes across 4,777 proteomes.
- Open source on GitHub with precompiled CUBINs; requires Python 3.12+ and per-chain A3M MSAs.
From the original article
4 min read at marktechpost.com
NVIDIA has detailed BioNeMo Inference Runtime (BioIR), a Python library that accelerates biomolecular structure-prediction models on NVIDIA GPUs while staying in plain PyTorch. In a matched benchmark on 1,000 human dimer targets across 8xH100 GPUs, BioIR-accelerated Boltz-2 delivered 58. 5K successfully folded residues per GPU-hour versus 20.
By Asif Razzaq · marktechpost.com
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