NVIDIA Aims To Change The Computing World With New A100 Line

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Excerpt

As more business and artificial intelligence workloads move to the cloud, it’s no surprise that the demand for computing resources remains unabated. Today’s data center must provide a combination of nearly infinite capacity and low latency processing. These requirements drive technology vendors such as ARM, Intel, and NVIDIA to innovate new chip designs and software platforms to support high-performance computing.

NVIDIA Aims To Change The Computing World With New A100 Line
As more business and artificial intelligence workloads move to the cloud, it's no surprise that the demand for computing resources remains unabated. Today's data center must provide a combination of nearly infinite capacity and low latency processing. These requirements drive technology vendors such as ARM, Intel, and NVIDIA to innovate new chip designs and software platforms to support high-performance computing.

There's a big pot of gold for the vendors who get it right. Research by Statistics forecasts the global data center chip market will grow to $15.64 billion U.S. dollars by 2025, more than double the size recorded in 2017. It was clear from NVIDIA's GTC event that the company plans on getting an outsized share of this enormous growth opportunity. NVIDIA, a company known to many for its Graphics Processing Units in gaming, also provides computing technology for data centers.

GPUs are not just for AI training anymore

It's focused heavily on artificial intelligence, which represents the most computationally intensive workloads in the data center. Most companies think of NVIDIA's GPUs as a go-to computing resource for training AI models using large datasets. And the company achieved great success in that market. Companies like Walmart have sworn by Nvidia's GPUs.

However, AI computing extends beyond training. Broadly speaking, there are several stages in machine learning that include the data preparation, training the model and the inference, and deploying models into production. The inference stage is where trained models are used to infer an outcome or result. While training is the sexy high-performance area of AI, the inference area is where companies leverage the fruits of training a model. Today, much of the inferencing work gets processed on Intel CPUs.

GPUs are expensive and weren't considered the right price for performance fit in the inferencing space. At its recent GTC conference, NVIDIA aimed to change the dialogue by showcasing how the company can accelerate the entire machine learning pipeline. As noted, GPUs were useful for the compute-intensive training in machine learning, but overkill for inference. Simultaneously, companies are also clamoring for more performance at the higher end of the data processing spectrum.

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