librmm-cu12
rmm - RAPIDS Memory Manager
Decision gist · record as of 2026-08-14
Yes, if you are building GPU-accelerated applications on CUDA 12 and need fine-grained memory management. The package is actively maintained, has no known vulnerabilities, and carries a permissive license. Install friction is moderate due to platform-specific wheels, but setup is straightforward on supported systems. Not applicable for CPU-only or non-NVIDIA GPU workflows.AI-flagged interpretation of the facts on this page — verify before relying
Before you install
- Requires CUDA 12.2+ and Volta architecture GPU (Compute Capability 7.0+); NVIDIA GPU drivers and CUDA toolkit must be installed on the system.
- Medium install friction due to platform-specific wheels (manylinux_2_24 and manylinux_2_28 for aarch64 and x86_64).
- Package is actively maintained with recent releases and depends only on rapids-logger.
License · maintenance · safety
Apache-2.0 (permissive) — Apache-2.0 permissive license allows use in commercial and proprietary projects with minimal restrictions, requiring only license attribution.
last release 2026-08-06 (8 days) · last repo commit 2026-08-14 · 709 stars
0 known vulnerabilities (OSV.dev, 2026-08-14) · 394,901 downloads/mo, #6,986 on PyPI
Alternatives
Verify before relying
pip install librmm-cu12
import librmm_cu12- Whether the package works on systems without NVIDIA GPUs or falls back gracefully.
- Exact Python version compatibility (requires_python field is unspecified in metadata).
- Whether rapids-logger dependency is optional or required at runtime.
- Specific API surface and memory allocation customization options available in version 26.8.0.
What it is and what it does
librmm-cu12 is the CUDA 12 variant of the RAPIDS Memory Manager, a library that provides a common interface for customizing GPU and host memory allocation. It addresses the performance requirements of GPU-centric workflows by allowing developers to use optimizations like pinned host memory for asynchronous transfers or device memory pools to reduce allocation overhead. The package wraps the C++ librmm library and exposes it to Python applications, making it suitable for projects built on the RAPIDS ecosystem that need fine-grained control over memory behavior on NVIDIA GPUs.
The library is designed for developers building GPU-accelerated data science and machine learning applications. It depends on rapids-logger for logging and requires a modern NVIDIA GPU (Volta or newer) and CUDA 12.2 or later. Installation uses platform-specific wheels, so setup is straightforward on supported Linux architectures, though the GPU and CUDA toolkit must already be present on the system.
Use it for
- Optimize asynchronous host-to-device memory transfers in GPU-accelerated data pipelines using pinned host memory.
- Reduce dynamic GPU memory allocation overhead in iterative machine learning training by using device memory pools.
- Customize memory allocation strategies in RAPIDS-based applications for specific workload patterns.
- Integrate custom memory management into C++ GPU applications via the RMM interface while maintaining Python compatibility.
- Profile and control memory usage patterns in multi-GPU workflows to avoid fragmentation and improve throughput.
Worth the install?
AI-flagged interpretation of the facts on this page. Verify before relying on it.
Yes, if you are building GPU-accelerated applications on CUDA 12 and need fine-grained memory management.
The package is actively maintained, has no known vulnerabilities, and carries a permissive license. Install friction is moderate due to platform-specific wheels, but setup is straightforward on supported systems. Not applicable for CPU-only or non-NVIDIA GPU workflows.
Install
librmm-cu12 on PyPI
Before you install
Medium install friction due to platform-specific wheels (manylinux_2_24 and manylinux_2_28 for aarch64 and x86_64). Package is actively maintained with recent releases and depends only on rapids-logger. Last release 8 days old; repository shows active development.
Requires CUDA 12.2+ and Volta architecture GPU (Compute Capability 7.0+); NVIDIA GPU drivers and CUDA toolkit must be installed on the system.
License in practice
Apache-2.0 permissive license allows use in commercial and proprietary projects with minimal restrictions, requiring only license attribution.
Quickstart
pip install librmm-cu12
import librmm_cu12Verify before relying
- Whether the package works on systems without NVIDIA GPUs or falls back gracefully.
- Exact Python version compatibility (requires_python field is unspecified in metadata).
- Whether rapids-logger dependency is optional or required at runtime.
- Specific API surface and memory allocation customization options available in version 26.8.0.
Package facts
| License | Apache-2.0 permissive |
| Python support | Not specified |
| Install friction | Medium. Platform-specific wheel |
| Runtime dependencies | 1 packagerapids-logger |
| Maintenance | Actively maintained 8 days since the last release |
| Last repo commit | |
| First released | |
| Downloads | 394,901 / month, #6,986 on PyPI 30-day window, as of 2026-08-14 |
| Known vulnerabilities | None known OSV.dev, checked 2026-08-14 |
| Classifiers | Environment :: GPU :: NVIDIA CUDAIntended Audience :: DevelopersProgramming Language :: C++Topic :: DatabaseTopic :: Scientific/Engineering |
Evidence: librmm_cu12-26.8.0-py3-none-manylinux_2_24_aarch64.manylinux_2_28_aarch64.whl; librmm_cu12-26.8.0-py3-none-manylinux_2_24_x86_64.manylinux_2_28_x86_64.whl
Tags
Let your AI agent find packages like this
Example. Real query, live index.
You found this page by searching. An agent finds it by wishing: SkillFed indexes 14,416 PyPI packages by what they can do, searchable in plain language.
wish › “device memory pool manager”
- librmm-cu12librmm-cu12 provides GPU memory allocation and management for CUDA 12…
- rmm-cu12RMM provides a common interface for customizing GPU and host memory…
- adafruit-circuitpython-connectionmanagerManages sockets and connections for CircuitPython devices using a…
Give your agent the search over MCP, or paste the wish link into any chat.
More Scientific/Engineering packages
NumPy provides an N-dimensional array object and a comprehensive suite of mathematical, linear algebra, Fourier transform, and random number functions for scientific computing in Python.
pandas provides fast, flexible data structures (Series and DataFrame) for loading, cleaning, transforming, and analyzing labeled or relational data in Python.
scipy provides numerical algorithms for mathematics, science, and engineering—including optimization, integration, linear algebra, Fourier transforms, signal and image processing, and ODE solvers—built on numpy arrays.
scikit-learn provides a comprehensive Python library for supervised and unsupervised machine learning, including classification, regression, clustering, dimensionality reduction, and model evaluation tools built on NumPy and SciPy.
Install it if you need to train, evaluate, or deploy supervised or unsupervised learning models.
dill extends Python's pickle module to serialize and deserialize a much wider range of Python objects, including functions, lambdas, classes, and interpreter sessions, to byte streams for storage or network transmission.
Multiprocess is an enhanced fork of Python's standard multiprocessing library that uses dill for better serialization, allowing you to spawn processes with a threading-like API and share complex objects between them.
Install it if you use multiprocessing and encounter pickle serialization limits with lambdas or complex objects.
See also rmm-cu12 · cymem · libraft-cu12 · umf · libucx-cu12 · raft-dask-cu12 · nvidia-cuda-cccl-cu12 · libcudf-cu12 · cpm-kernels · rapids-logger