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CV-CUDA™ is an open-source library that enables building high-performance, GPU-accelerated pre- and post-processing for AI computer vision applications in the cloud at reduced cost and energy.

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AI Computer Vision

Use Cases

Explore common use cases with AI imaging and computer vision workloads deployed at scale in the cloud.

Image Understanding

Image understanding involves AI algorithms interpreting and processing visual data to recognize patterns, objects, and context, paving the way for applications like facial recognition, medical imaging, and scene understanding.

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Generative AI

Generative AI algorithms produce new content or data, imitating the patterns they learn from existing data, enabling tas
ks like image creation, text generation, and style transfer.

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3D Worlds

3D worlds are digital environments that represent space in three dimensions, offering immersive experiences for users in gaming, simulations, and virtual reality platforms.

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HD Mapping

HD mapping creates highly detailed digital representations of the physical world, essential for the precision and decision-making processes of autonomous vehicles.

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Key Features

CV-CUDA provides a specialized set of 45+ highly performant computer vision and image processing operators. CV-CUDA also offers:

  • C, C++, and Python APIs
  • Batching support, with variable shape images
  • Zero-copy interfaces to deep learning frameworks like PyTorch and TensorFlow
  • An NVIDIA Triton™ Inference Server example using CV-CUDA and NVIDIA® TensorRT™
  • End-to-end GPU-accelerated object detection, segmentation, and classification examples

View a full list of the operators in the CV-CUDA documentation.

CV-CUDA Library

CV-CUDA Benefits

Computer Vision Cloud Applications

Specialized Set of Kernels for Cloud-Based Use Cases

Computer Vision Operators

Efficient, Hand-Optimized Kernels That Save Cost and Energy

CV-CUDA Integration

Lightweight and Flexible for Integrating Into Frameworks

Up to 49X End-to-End Throughput Improvement

CV-CUDA lets you move your bottlenecked pre- and post-processing pipelines from the CPU to the GPU, boosting throughput for complex workflows.

For a typical video segmentation pipeline, CV-CUDA enabled an end-to-end 49X speedup using NVIDIA L4 Tensor Core GPUs. With the latest and most efficient NVIDIA GPUs and CV-CUDA, developers of cloud-scale applications can save tens to hundreds of millions in compute costs and eliminate thousands of tons in carbon emissions.

Video Segmentation Pipeline (End-to-End)

1080p, 30fps


CV-CUDA is interoperable with the following libraries, SDKs, and frameworks.

Global Industry Adoption

From content understanding to visual search and generative AI, customers are adopting CV-CUDA for their AI computer vision use cases.

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In the News

Computer Vision Segmentation Pipeline

Increasing Throughput and Reducing Cost for AI-Based Computer Vision With CV-CUDA

CV-CUDA enables real-time, high-performance cloud-scale applications with demands for lower latency and higher throughput.

Read how CV-CUDA increases throughput while also reducing both cost and energy consumption
CV-CUDA for Visual Search

NVIDIA Announces Microsoft, Tencent, Baidu Adopting CV-CUDA for Computer Vision AI

CV-CUDA is helping customers build and scale AI-based imaging and computer vision pipelines.

Read how industry leaders are adopting CV-CUDA
CV-CUDA Video Application

NVIDIA Introduces Open-Source Project to Accelerate Computer Vision Cloud Applications

CV-CUDA combines accelerated image pre- and post-processing algorithms and tools to process higher image throughput and lower cloud computing cost.

Read how CV-CUDA can accelerate pre- and post-processing pipelines

Videos and Webinars

Help make CV-CUDA better by sharing your feedback and how you’re using it. We may follow up with you to continue the conversation.

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