Cloud-Scale AI Computer Vision Use Cases
Common use cases with AI imaging and CV workloads deployed at scale in the cloud include:
CV-CUDA provides a specialized set of 45+ highly performant computer vision and image processing operators. CV-CUDA also offers:
How CV-CUDA Differs From Other Computer Vision Libraries
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 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)
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
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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