---
id: rmm-cu12
version: "26.8.0"
license: Apache-2.0
license_treatment: permissive
maintenance: active
---

# rmm-cu12 — rmm - RAPIDS Memory Manager

License: permissive · Maintenance: active · Downloads: 429.9K/mo

## What it is and what it does

RMM is a memory management library for GPU-accelerated workflows that sits between your application and CUDA's memory allocator. It provides a pluggable interface for customizing how device memory and host memory are allocated, enabling strategies like device memory pooling (to reduce allocation overhead) and pinned host memory (for faster asynchronous transfers). The library is part of the RAPIDS ecosystem and is designed for applications that need fine-grained control over memory behavior to achieve optimal GPU performance.

The package depends on cuda-bindings, librmm-cu12 (the compiled C++ library), and numpy. Installation requires a CUDA 12.2+ environment and works with Python 3.11 through 3.14. It is actively maintained and has no known security vulnerabilities.

Use it for:

- Reduce GPU memory allocation overhead in tight loops by using a device memory pool sub-allocator.
- Enable asynchronous host-to-device transfers by allocating pinned host memory through RMM.
- Integrate custom memory allocation strategies into RAPIDS libraries like cuDF or cuML.
- Profile and optimize memory usage patterns in GPU-accelerated data processing pipelines.
- Build C++ applications that need portable, customizable GPU memory management across different NVIDIA architectures.

## Worth the install?

AI-flagged interpretation of the facts above — verify before relying.

RMM provides a common interface for customizing GPU and host memory allocation, with implementations for device memory pooling, pinned host memory, and other allocation strategies.

Yes, if you are building GPU-accelerated applications with CUDA 12.2+ and need to optimize memory allocation behavior. The active maintenance, permissive license, and zero known vulnerabilities make it a safe choice. Install friction is moderate due to compiled dependencies and CUDA requirements, but this is expected for GPU libraries. Not necessary for simple GPU workloads that do not require custom memory strategies.

## Install

pip install rmm-cu12
uv add rmm-cu12
poetry add rmm-cu12

## Installing rmm-cu12

Before you install: Medium install friction due to compiled wheels and CUDA runtime dependencies. Active maintenance with recent releases (8 days since last update) and a stable repository.

License in practice: Apache-2.0 permissive license allows commercial and private use with minimal restrictions.

Quickstart:

pip install rmm-cu12

import rmm
from rmm.allocators import CudaMemoryResource

# Use RMM's memory resource for GPU allocation
rmm.reinitialize(allocator='cuda')

Requires CUDA 12.2+ and a compatible NVIDIA GPU (Volta architecture or newer). The cu12 variant is specific to CUDA 12.x; other CUDA versions require different rmm packages.

Verify before relying:

- Whether the package works with CUDA versions other than 12.x (the cu12 suffix suggests CUDA 12 specificity)
- Performance characteristics compared to default CUDA memory allocation
- Compatibility with non-Volta GPU architectures despite documentation stating Volta+ support

## Package facts

- License: Apache-2.0 (permissive)
- Python support: supports_current
- Install friction: medium
- Maintenance: active
- Downloads: 429.9K/month (top 15,000 on PyPI)
- Known vulnerabilities: none known

## Tags

gpu memory allocation, cuda memory management, device memory pooling, pinned host memory, rapids memory manager, gpu memory optimization, cuda memory pool, gpu-memory-management, cuda, rapids

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