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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →To run OpenMM on a GPU, install a backend that matches your hardware—CUDA for NVIDIA, HIP for ROCm-compatible AMD, or OpenCL where supported—then verify the installation and explicitly select the platform if needed. OpenMM’s User Guide 8.6 documents conda-forge and pip routes; package extras, drivers, and runtime support can change, so check the current installation guide before copying a command. The instructions below reflect that guide as accessed October 4, 2026.
Choose the GPU backend that matches your hardware
OpenMM 8.6 lists five platforms: Reference, CPU, CUDA, OpenCL, and HIP. CUDA is the documented choice for NVIDIA GPUs. HIP is recommended for ROCm-compatible AMD GPUs; OpenCL is another option and can support a range of GPU and CPU devices. The OpenMM library guide says AMD OpenCL is usually slower than HIP. Reference prioritizes simplicity over performance, while CPU is often practical when a fast GPU is unavailable; a custom force workload may behave differently. See the OpenMM platform overview for platform details.
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- NVIDIA: choose CUDA and install a current NVIDIA driver.
- AMD: choose HIP on ROCm-compatible hardware and install current AMD drivers plus HIP/ROCm. OpenCL is an alternative where supported.
- Other OpenCL-capable devices: OpenCL may be available, including on some Intel or Apple GPUs as described in the guide; availability depends on the device and its software support.
Installing OpenMM does not establish that a particular device or workload will benefit from GPU execution. Compatibility depends on the backend, operating system, driver/runtime, and simulation.
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1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesInstall OpenMM
The OpenMM 8.6 guide documents both conda-forge and pip. Use one package-management route in the environment where you intend to run your Python simulation, and follow the live guide for the currently supported backend/runtime combinations.
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Option 1: conda-forge
Install the package with:
conda install -c conda-forge openmmAccording to the guide, recent conda versions install an OpenMM build using the latest CUDA version supported by the drivers. To request a CUDA build explicitly, its example is:
conda install -c conda-forge openmm cuda-version=12The guide says its conda packages are built for CUDA 12 and above and warns that CUDA releases are not binary compatible: OpenMM must match the CUDA version it was compiled with. Treat the example as version-specific, not a permanent setting; check the current guide for supported combinations.
Option 2: pip
The base package and documented GPU extras are:
pip install openmm
pip install 'openmm[cuda12]'
pip install 'openmm[hip6]'The OpenMM 8.6 guide also lists the CUDA 13 extra and HIP 7 extra. Use the extra matching the supported backend and runtime for your system. The base pip package includes OpenCL, CPU, and Reference; request CUDA or HIP with the corresponding extra. Keep the quotes around bracketed extras in shells that might interpret brackets. Consult the current guide for exact extras and prerequisites.
Install drivers and verify the installation
Install current drivers from the GPU vendor before diagnosing why OpenMM does not find a device. In the documented package route, CUDA is installed automatically; NVIDIA drivers are still required. For AMD’s recommended HIP backend, the guide calls for current AMD drivers and HIP/ROCm. It also notes that macOS includes OpenCL.
Run the installation check in the same Python environment where OpenMM was installed:
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python -m openmm.testInstallationThis check confirms that OpenMM is installed, reports whether GPU acceleration is available through CUDA, OpenCL, and/or HIP, and checks consistency across platforms. It is an installation check—not a benchmark and not proof that a later simulation uses a particular GPU.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Make a simulation use the intended platform
OpenMM ordinarily tries to select the fastest available platform. To override that choice, set OPENMM_DEFAULT_PLATFORM or pass a platform object when constructing the simulation. The following fragment illustrates explicit CUDA selection; it assumes your program has already created topology, system, and integrator and imported the relevant OpenMM classes:
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platform = Platform.getPlatform('CUDA')
simulation = Simulation(topology, system, integrator, platform)Replace CUDA with the platform appropriate to your installation if you are using HIP or OpenCL. Explicit selection makes the requested platform clear; it does not make an unsupported device or incompatible driver work. For the broader simulation API and platform controls, see OpenMM’s Running Simulations chapter.
Installation is not a complete molecular-dynamics protocol
These steps install OpenMM, check backend availability, and show how to request a platform. They do not define how to prepare a molecule, choose a force field, solvate or minimize a system, equilibrate it, select an ensemble or timestep, apply restraints, or analyze trajectories. Those choices depend on the molecular system and scientific question; a GPU-enabled run is not by itself evidence that a protocol is scientifically appropriate. Use the current official running-simulations guide alongside system-specific methods for those decisions.
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