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NVIDIA's fascinating role in artificial intelligence

On the 30th of September 2012, something significant changed in the world of computing.

NVIDIA in 2006 had launched its CUDA software library and API to enable alternative uses of its cards, but the ImageNet competition win in 2012 brought GPU usage for AI into the mainstream.

This helps data scientists and researchers iterate faster to create new AI models and solve many different types of use cases.

"When these AI models are deployed in applications like conversational AI, understanding video, and providing recommendations, the NVIDIA platform enables real-time processing and efficiently serves them to billions of users,"

The company has also been hard at work fine-tuning its hardware, with the introduction of specialised cores for TensorFlow, which is a commonly used open-source library in ML.

Together with our ecosystem, we're very focused on energy efficiency at all levels of design -- from processor design to systems, supercomputers, and full-scale data centres.

In addition to the CUDA software libraries, NVIDIA also has VGrid and VComputeServer tools that help to run and distribute these huge GPU workloads across a bunch of servers and GPU cards.

"With vComputeServer, IT admins can better streamline management of GPU-accelerated virtualised servers while retaining existing workflows and lowering overall operational costs.

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