{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/4"},{"label":"Database","url":"https://skillfed.io/packages/category/database/3"}],"enrichment":{"capability":"librmm-cu12 provides GPU memory allocation and management for CUDA 12 workloads, offering customizable device and host memory pools to optimize GPU-centric applications.","skillfed_tags":["gpu-memory-management","cuda","rapids"],"use_cases":["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."],"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.\n\nThe 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.","worth_installing":"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."},"id":"librmm-cu12","links":{"html":"https://skillfed.io/packages/librmm-cu12","md":"https://skillfed.io/packages/librmm-cu12.md","pypi":"https://pypi.org/project/librmm-cu12/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-08-06","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"librmm-cu12","python_support":"unspecified","summary":"rmm - RAPIDS Memory Manager"},"popularity":{"monthly_downloads":394901,"position":6986,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"26.8.0"}