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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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Cloud-Scale AI Computer Vision Use Cases

AI Computer Vision

Common use cases with AI imaging and CV workloads deployed at scale in the cloud include:

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
  • Triton Inference Server™ example using CV-CUDA and TensorRT™
  • End-to-end GPU-accelerated object detection, segmentation, and classification examples
CV-CUDA Library

How CV-CUDA Differs From Other Computer Vision Libraries

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 enables you to move your pre- and post-processing pipelines that are bottlenecked on the CPU to the GPU, helping achieve higher throughput for complex workflows. For a typical video segmentation pipeline, CV-CUDA enabled achieving 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

Global Industry Adoption

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

NVIDIA Partner for CV-CUDA
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NVIDIA Partner for CV-CUDA

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 and inform our roadmap by sharing how you are using CV-CUDA and your feedback about the open-source library. We may follow-up with you to engage further.

Share Your Use Case

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