cuequivariance-ops-torch-cu12
cuequivariance-ops-torch - GPU Accelerated Torch Extensions for Equivariant Primitives
Decision gist · record as of 2026-08-14
Yes, with conditions. Install if you are building equivariant neural networks on NVIDIA GPUs and need production-grade inference performance, or if you are already using cuEquivariance and want direct kernel access. Do not install if you lack CUDA 12 hardware, need cross-platform support, or require an open-source-compatible license. The proprietary restrictions and hardware lock-in are significant trade-offs for the performance gains.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires CUDA 12 and NVIDIA GPU; platform-specific wheels for x86_64 or aarch64 Linux only
- Medium install friction due to platform-specific wheels (x86_64 and aarch64 Linux only, CUDA 12 required).
- Active maintenance with release 7 days ago.
License · maintenance · safety
(unclear) — NVIDIA proprietary license with significant restrictions: non-transferable, no sublicensing, reverse engineering prohibited, and cannot be used in open-source contexts. Distribution requires material additional functionality beyond the SDK. Use in critical applications is explicitly prohibited and indemnified.
last release 2026-08-07 (7 days)
0 known vulnerabilities (OSV.dev, 2026-08-14) · 122,622 downloads/mo, #11,944 on PyPI
Alternatives
Verify before relying
pip install cuequivariance-ops-torch-cu12
import cuequivariance_ops_torch
import torch
# Access kernels as torch.nn.Module or torch.library operators
# See module docstrings for complete input contracts- Performance characteristics and speedup claims for specific model architectures or batch sizes
- Compatibility with specific PyTorch versions beyond Python 3.10–3.14 support
- Whether the package works on systems without NVIDIA GPUs or only on NVIDIA hardware
What it is and what it does
cuequivariance-ops-torch-cu12 is a PyTorch extension that wraps optimized CUDA kernels for equivariant neural network operations. It is designed primarily as a lower-level component accessed through cuEquivariance, though the kernels can be used directly. The package exposes operations as both torch.nn.Module objects (for training and model export) and torch.library custom operators (for torch.compile tracing and TensorRT inference).
The package includes specialized primitives for Pairformer inference, such as attention pair bias masking and combined triangle multiplication. These operations are tuned for specific configurations (BF16 precision, D=256, H=16, sequence lengths 384 or 512) and use native low-precision tensor-core products with FP32 accumulation. It requires CUDA 12, torch, and scipy, and is distributed as platform-specific wheels for x86_64 and aarch64 Linux only.
Use it for
- Accelerate Pairformer inference in equivariant models using optimized CUDA kernels
- Export equivariant neural network models via torch.export for deployment with TensorRT on NVIDIA GPUs
- Integrate low-precision (BF16/FP16) equivariant operations into production inference pipelines
- Access raw CUDA kernels for custom equivariant layer implementations beyond the standard API
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, with conditions.
Install if you are building equivariant neural networks on NVIDIA GPUs and need production-grade inference performance, or if you are already using cuEquivariance and want direct kernel access. Do not install if you lack CUDA 12 hardware, need cross-platform support, or require an open-source-compatible license. The proprietary restrictions and hardware lock-in are significant trade-offs for the performance gains.
Install
cuequivariance-ops-torch-cu12 on PyPI
Before you install
Medium install friction due to platform-specific wheels (x86_64 and aarch64 Linux only, CUDA 12 required). Active maintenance with release 7 days ago. Requires torch and scipy as runtime dependencies.
Requires CUDA 12 and NVIDIA GPU; platform-specific wheels for x86_64 or aarch64 Linux only
License in practice
NVIDIA proprietary license with significant restrictions: non-transferable, no sublicensing, reverse engineering prohibited, and cannot be used in open-source contexts. Distribution requires material additional functionality beyond the SDK. Use in critical applications is explicitly prohibited and indemnified.
Quickstart
pip install cuequivariance-ops-torch-cu12
import cuequivariance_ops_torch
import torch
# Access kernels as torch.nn.Module or torch.library operators
# See module docstrings for complete input contractsVerify before relying
- Performance characteristics and speedup claims for specific model architectures or batch sizes
- Compatibility with specific PyTorch versions beyond Python 3.10–3.14 support
- Whether the package works on systems without NVIDIA GPUs or only on NVIDIA hardware
Package facts
| License | Not declared unclear |
| Python support | Supports the current Python release >=3.10 |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 3 packagescuequivariance-ops-cu12scipytorch |
| Maintenance | Actively maintained 7 days since the last release |
| First released | |
| Downloads | 122,622 / month, #11,944 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Intended Audience :: DevelopersProgramming Language :: PythonProgramming Language :: Python :: 3.10Programming Language :: Python :: 3.11Programming Language :: Python :: 3.12Programming Language :: Python :: 3.13Programming Language :: Python :: 3.14 |
Evidence: cuequivariance_ops_torch_cu12-0.11.1-cp310-cp310-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; cuequivariance_ops_torch_cu12-0.11.1-cp310-cp310-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; cuequivariance_ops_torch_cu12-0.11.1-cp311-cp311-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; cuequivariance_ops_torch_cu12-0.11.1-cp311-cp311-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; cuequivariance_ops_torch_cu12-0.11.1-cp312-cp312-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; cuequivariance_ops_torch_cu12-0.11.1-cp312-cp312-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; cuequivariance_ops_torch_cu12-0.11.1-cp313-cp313-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; cuequivariance_ops_torch_cu12-0.11.1-cp313-cp313-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl; cuequivariance_ops_torch_cu12-0.11.1-cp314-cp314-manylinux_2_26_aarch64.manylinux_2_28_aarch64.whl; cuequivariance_ops_torch_cu12-0.11.1-cp314-cp314-manylinux_2_27_x86_64.manylinux_2_28_x86_64.whl
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See also causal-conv1d · cuequivariance-ops-cu12 · cuequivariance-ops-cu13 · cuequivariance-torch · cuequivariance · mace-torch · hoptorch · slangtorch · e3nn · pytorch-seed