NVIDIA CV-CUDA (original) (raw)
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.
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.
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.
HD Mapping
HD mapping creates highly detailed digital representations of the physical world, essential for the precision and decision-making processes of autonomous vehicles.
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
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.
In the News
Videos and Webinars
Additional Resources
- Watch Webinar: Overcoming Pre- and Post-Processing Bottlenecks in AI Computer Vision Pipelines (42:27 Minutes)
- See the Difference CV-CUDA Makes: Runway Optimizes AI Image and Video Generation Tools With CV-CUDA (01:18 Minutes)
- Review Documentation: CV-CUDA Developer Guide
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