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SPDX-FileCopyrightText: 저작권 © 2026 NVIDIA CORPORATION & AFFILIATES. 모든 권리 보유

SPDX-License-Identifier: 아파치-2.0

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BioNeMo Inference 실행 시간

쉽고 빠르게, 메모리 효율적인 구조 예측

단백질, 핵산, 석회화 구조용 GPU 가속 예측 모델 - FASTA / MSA에서 PDB / mmCIF로.

소개

BioNeMo Inference Runtime (BioIR)는 NVIDIA의 구조 예측 라이브러리입니다. 관련 기사 다섯 단계 GPU 파이프 라인은 AlphaFold-lineage와 all-atom 모델을 회전 신뢰 점수를 가진 PDB/mmCIF로. 모형은 정규적인 nn.Module를 체재합니다 — no TensorRT 엔진 빌드.

면책

BioIR는 베타 상태에, 완전히 기능적이지 않을지도 모릅니다, 과실 또는 포함할지도 모릅니다 디자인 결함, 그리고 예고 없이 언제든지 변경 될 수 있습니다. 감사합니다. 우리의 제품에 개량하고 iterate에 의견.

시작하기

공지 사항 및 불만 사항은 별도로 적용됩니다. 의존성, 모델 체크포인트 및 메타데이터 다운로드

# 필수품

• Linux, x86 64 또는 aarch64, NVIDIA GPU와 함께. 바퀴는 입니다 manylinux_2_34, 그래서 호스트 필요 glibc 2.34 또는 더 새로운 - 우분투 22.04, RHEL 9 또는 나중에.
• ** 드라이버 580 또는 더 새로운.

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SPDX-FileCopyrightText: Copyright © 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved.

SPDX-License-Identifier: Apache-2.0

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About

BioNeMo Inference Runtime (BioIR) is NVIDIA’s library for structure-prediction inference. A five-stage GPU pipeline turns AlphaFold-lineage and all-atom models into PDB/mmCIF with confidence scores. Models stay ordinary nn.Modules — no TensorRT engine build.

Disclaimer

BioIR is in a Beta state, may not be fully functional, may contain errors or design flaws, and may be changed at any time without notice. We appreciate your feedback to improve and iterate on our products.

Getting Started

The notice and disclaimer applies to separately downloaded dependencies, model checkpoints, and metadata.

Prerequisites

• Linux, x86_64 or aarch64, with an NVIDIA GPU. The wheels are manylinux_2_34, so the host needs glibc 2.34 or newer — Ubuntu 22.04, RHEL 9 or later.
• Driver 580 or newer. The dev image carries a CUDA 13.2 build of PyTorch. An older driver runs it only through the forward-compatibility shim, which we have measured hanging and crashing part-way through a run rather than merely running slowly — results taken on one are discarded, not corrected.
• CPython 3.12, 3.13, or 3.14. Each architecture’s wheel uses the cp312-abi3 stable ABI and supports all three versions.
• Docker and the NVIDIA Container Toolkit, to build from source in the dev container. Installing the wheel needs neither.

PyTorch and the CUDA math libraries arrive as wheel dependencies, or in nvcr.io/nvidia/pytorch:26.05-py3 when you use the container. Building the extension from source outside a container needs a C++17 compiler and CUDA headers as well — docs/dev.md.

Supported GPUs

BioIR runs on the Ampere, Ada Lovelace, Hopper, and Blackwell GPUs listed in the support matrix. The doc also shows which fused kernels apply to each architecture. Benchmarks page shows detailed speedup, memory, and accuracy for these GPUs: H200, H100, A100, L40S, GB200, and GB300.

Install

BioIR is published on PyPI, one wheel per CPU architecture:

pip install bionemo-ir

The wheel ships the kernels precompiled. It needs the driver’s libcuda.so.1,