arXiv:2607.05439v1 Announce Type: new Abstract: Many all-atom generative protein models can in principle design large multimeric complexes by jointly modelling all chains, but their quadratic token- and atom-pair representations quickly exceed single-GPU memory as the number of chains and residues modelled grows. We introduce Design-CP, two context-parallel (CP) inference strategies for RFdiffusion 3 (1D row-sharding and 2D grid sharding with ring attention) that distribute the quadratic activations across a multi-GPU mesh while preserving pretrained weights. We characterise their scaling when
The increasing complexity and computational demands of generative protein models necessitate new parallel inference strategies to overcome hardware limitations. This research emerges as a direct response to the quadratic scaling issues encountered in state-of-the-art protein design.
This development significantly enhances the scalability of AI models for protein design, enabling the creation of larger and more complex molecular structures critical for synthetic biology applications. It accelerates the pace of innovation in drug discovery, biomaterials, and energy solutions by making previously intractable designs feasible.
The ability to design large multimeric protein complexes efficiently using multi-GPU setups means that the practical limits of AI-driven protein engineering are substantially expanded. This opens doors for advanced applications that require atomic-level precision over vast molecular architectures.
- · Biopharmaceutical industry
- · Material science researchers
- · GPU manufacturers
- · AI compute infrastructure providers
- · Traditional protein engineering methods
- · Companies reliant on slow or single-GPU computational approaches
Computational bottlenecks for all-atom generative protein design are significantly reduced, accelerating research.
This improved capability leads to a surge in the development of novel protein-based therapeutics, diagnostics, and materials.
The enhanced design capacity could enable new classes of bio-manufactured products, potentially disrupting sectors from healthcare to construction.
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Read at arXiv cs.LG