Efficient protein structure prediction from compact computers to datacenters with OpenFold-TRT

2026 · doi:10.64898/2026.03.11.711233
preprint OA: closed
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Abstract

We introduce accelerations for deep learning inference with OpenFold and TensorRT that, combined with MMseqs2-GPU on an x86 system with one NVIDIA RTX PRO 6000 Blackwell Server Edition GPU, reach up to 131× faster inference compared to AlphaFold2. ARM-optimizations enable homology search on power efficient DGX Spark, and allow search beyond available GPU RAM on NVIDIA Grace Hopper Superchip at comparable overall folding speed. These accelerations enable high-throughput protein structure inference at no accuracy cost using familiar tools.
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Abstract We introduce accelerations for deep learning inference with OpenFold and TensorRT that, combined with MMseqs2-GPU on an x86 system with one NVIDIA RTX PRO 6000 Blackwell Server Edition GPU, reach up to 131× faster inference compared to AlphaFold2. ARM-optimizations enable homology search on power efficient DGX Spark, and allow search beyond available GPU RAM on NVIDIA Grace Hopper Superchip at comparable overall folding speed. These accelerations enable high-throughput protein structure inference at no accuracy cost using familiar tools. Competing Interest Statement The authors declare the following financial inter- ests/personal relationships which may be consid- ered as potential competing interests: K.D., P.S., F.B., B.F., A.N., R.O., M.E., J.C., A.K., A.Co., A.V., C.D., A.Ch. are employed by NVIDIA. M.S. declares an outside interest in Stylus Medicine.

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last seen: 2026-05-20T01:45:00.602351+00:00