CV-CUDA
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.
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 Benefits
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Specialized Set of Kernels for Cloud-Based Use Cases
Efficient, Hand-Optimized Kernels That Save Cost and Energy
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
Interoperability
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
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.
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.
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.
Videos and Webinars
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