NVIDIA CUDA-X

NVIDIA GPU-accelerated libraries powering the world’s most advanced AI and accelerated computing platforms.

Overview
Libraries
Success Stories

Overview
Libraries
Success Stories

From generative AI and data analytics to quantum chemistry and climate modeling, breakthrough applications demand immense computational power. While parallel programming can be incredibly complex, NVIDIA CUDA-X™ libraries remove the barrier to hardware-level optimization.

Built on the production-proven CUDA® platform and its two decades of computing leadership, CUDA-X’s highly optimized libraries provide the foundational algorithms and essential computational routines developers need to easily build, deploy, and scale workloads.

By shifting the complexity of low-level GPU programming to drop-in libraries, CUDA-X delivers a future-proof engine that unlocks continuous performance gains across every major industry.

Libraries

Accelerate With CUDA-X

Whether building new pipelines or accelerating existing ones, teams can leverage CUDA-X’s hundreds of libraries to easily optimize, deploy, and scale workloads across data processing, AI, deep learning, quantum computing, high-performance computing (HPC), physical sciences, and more to instantly secure hardware-level efficiency and continuous performance updates.

CUDA Math Libraries

The Foundation of HPC and AI: Powers heavy computing tasks like medical imaging and fluid simulations.
Drop-In Acceleration: Provides instant GPU speeds without the need to rewrite core application code.
Scale and Versatility: Combines core math libraries, including linear algebra and accelerated solvers, with highly performant Pythonic APIs that scale from a workstation to a supercomputer.

Scientific Computing Libraries

Accelerated Discovery: Powers breakthrough research in molecular structures, quantum chemistry, and advanced materials.
Next-Gen Manufacturing: Optimizes semiconductor design and GPU-accelerated computational lithography.
Domain-Specific AI: Includes tools like Python libraries and NVIDIA NIM™ microservices for physics-aware neural networks.

Physics Libraries

Faster Simulations: Delivers high-speed GPU acceleration across computational, quantum, and multiphysics domains.
Physics-Aware AI: Features frameworks like NVIDIA PhysicsNeMo™ and Warp to build, train, and scale AI simulation models.
Global Weather Modeling: Provides access to professional-grade weather models and climate AI through NVIDIA Earth-2.

Quantum Computing Libraries

Faster Simulations: Delivers highly optimized routines to speed up quantum computing simulations and HPC integration.
Secure Workflows: Features cuPQC to accelerate and optimize next-generation, post-quantum cryptography.
Hybrid and Error Optimization: Provides advanced, GPU-accelerated solvers and error-mitigation libraries for hybrid quantum-classical algorithms.

Deep Learning Core Libraries

Core Neural Networks: Powers deep learning applications with optimized building blocks via cuDNN™.
Inference Optimization: Delivers maximum production deployment performance using NVIDIA TensorRT™ and TensorRT-LLM.
Custom Kernel Building: Provides modular templates like CUTLASS and FlashInfer to maximize Tensor Core efficiency.

Parallel Algorithm Libraries

High-Level GPU Code: Delivers powerful, C++ Standard Template Library (STL)-based parallel algorithms through Thrust to simplify GPU acceleration.
Low-Level Hardware Efficiency: Provides collective primitives via CUB for precise warp, block, and device-wide execution control.
Python and Architecture Optimization: Features native Python interfaces and standardized distributed primitives to optimize sorting, scanning, and reduction patterns.

Data Processing Libraries

Zero-Code Acceleration: Speeds up existing tabular data and machine learning workflows in pandas, Polars, scikit-learn, and Apache Spark without code changes.
Scale and Graph Analytics: Scales graph analytics, vector search, and complex decision optimization using engines like cuGraph, cuVS, and cuOpt™.
Pipeline and Storage Efficiency: Maximizes data throughput for cybersecurity, generative AI curation, and storage transfers via NVIDIA Morpheus, NeMo™ Curator, and GPUDirect® Storage.

Image and Video Libraries

High-Throughput Codecs: Accelerates video encoding, decoding, and pixel motion tracking via dedicated hardware SDKs.
AI Pipeline Processing: Speeds up data loading and pre- and post-processing for vision AI workloads using NVIDIA® DALI® and CV-CUDA™.
Advanced Image Manipulation: Optimizes 2D signal processing and massive multidimensional datasets for biomedical and geospatial applications.

Communication Libraries

Scaled Architecture Primitives: Maximizes bandwidth and maintains low latency for fast multi-GPU and multi-node communication via NVIDIA Collective Communications Library (NCCL).
Global Memory Spaces: Provides a partitioned global address space across clustered GPU memories using the NVSHMEM model.
Low-Latency Inference Transfer: Moves KV cache and tensors efficiently between GPUs, storage, and memory tiers via NVIDIA Inference Transfer Library (NIXL).

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Success Stories

Real-World Impact

See how leading enterprises are using NVIDIA CUDA-X libraries to solve the world's most complex computing, engineering, and AI challenges.

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