Biomolecular structure prediction and co-folding with models like OpenFold3 are now mainstream, large-scale workloads powering drug discovery and protein design. Increasingly, they’re driven end-to-end by AI agents. For an agent to run that pipeline well, every step needs to be fast and scalable: Multiple Sequence Alignment (MSA) generation, co-folding inference, serving, and multi-GPU scale-out. A bottleneck anywhere limits overall throughput.

Speed and memory-efficiency are critical for key drug discovery workflows such as virtual screening and prediction of large molecular assemblies. In virtual screening, millions to billions of compounds are screened against one or a few protein targets. While co-folding models often give the best predicted structures, they can be expensive to run, making them impractical for virtual screening applications.…