Design-CP: Context Parallelism for Protein Nanoparticle Design
Design-CP introduces two context-parallel inference strategies for RFdiffusion 3—1D row-sharding and 2D grid sharding with ring attention—to distribute quadratic activations across multiple GPUs, enabling the design of large protein nanoparticles on limited memory. The 2D sharding shows better wall-clock scaling for icosahedral assemblies and octahedral design is demonstrated on workstation-grade 16GB GPUs.
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[Submitted on 3 Jul 2026]
Title:Design-CP: Context Parallelism for Design of Protein Nanoparticles
View a PDF of the paper titled Design-CP: Context Parallelism for Design of Protein Nanoparticles, by Lorenzo Tarricone and 3 other authors
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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 sampling icosahedral assemblies, showing that the maximum feasible asymmetric subunit (ASU) size grows with the expected square-root trend in GPU count and that 2D sharding achieves better wall-clock scaling. Moreover, we show how strong point-group symmetry constraints make CP usable out of the box for end-to-end, all-atom design of icosahedral nanoparticles, yielding favourable in silico structural and interface metrics. Finally, we demonstrate octahedral nanoparticle design on a small cluster of workstation-grade 16GB GPUs, illustrating how Design-CP can be a practical path towards democratising large-assembly protein design.
Comments: Accepted at the 2026 Workshop on Generative and Agentic AI for Biology (ICML 2026)
Subjects:
Machine Learning (cs.LG); Distributed, Parallel, and Cluster Computing (cs.DC); Quantitative Methods (q-bio.QM)
Cite as: arXiv:2607.05439 [cs.LG]
(or arXiv:2607.05439v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2607.05439
arXiv-issued DOI via DataCite
Submission history
From: Lorenzo Tarricone [view email] [v1] Fri, 3 Jul 2026 15:50:24 UTC (8,922 KB)
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