AI and the need for purpose-built cloud infrastructure

AI and the need for purpose-built cloud infrastructure

Modern AI solutions augment human understanding, preferences, intent, and even spoken language. AI enhances our knowledge and understanding by delivering faster, more informed insights that fuel transformation beyond anything we’ve ever imagined. The challenge of this rapid growth and transformation is that the demand for AI computing power exceeds Moore’s Law in computing advancements.

AI requires an infrastructure that can meet the ever-increasing computing power demands and specialized needs of AI applications and workloads, such as natural language processing, robotic process automation, machine learning machine and deep learning.

High performance computing provides scalable solutions for AI.

To operate at today’s much higher levels of demand, AI infrastructure must scale to take advantage of single servers with multiple accelerators and scale to combine many such servers distributed across a high-performance network.

Large-scale AI computing infrastructure combines the memory of individual graphics processing units (GPUs) into a large shared pool to process larger and more complex models. Combined with the incredible vector processing capabilities of GPUs, high-speed memory pools have proven to be extremely efficient in processing large, multi-dimensional arrays of data.

With the added capacity of a high-bandwidth, low-latency interconnect fabric, the scalable AI-driven infrastructure can dramatically accelerate time-to-egress. This is achieved through advanced parallel communication methods, interweaving computation and communication across a large number of compute nodes.

Cloud infrastructure purpose-built for AI

Microsoft Azure is currently the only global public cloud service provider that delivers purpose-built AI supercomputers with massively scalable scale-up and scale-out computing infrastructure comprised of interconnected NVIDIA Ampere A100 Tensor Core NVIDIA Quantum InfiniBand GPUs. Azure Machine Learning provides an enterprise-grade service for the end-to-end machine learning lifecycle, accelerating the integration of AI into workloads to drive smarter simulations and accelerate intelligent decision-making .

Scale-up and scale-out infrastructures powered by NVIDIA GPUs and NVIDIA Quantum InfiniBand networking rank among the most powerful supercomputers on the planet. Microsoft Azure has ranked in the top 15 Top500 supercomputers in the world and currently five systems in the top 50 use Azure infrastructure with NVIDIA A100 Tensor Core GPUs. Twelve of the top twenty supercomputers on the Green500 list use NVIDIA A100 Tensor Core GPUs.

This supercomputer-class AI infrastructure is accessible to researchers and developers in organizations of all sizes around the world and is used by customers in all industry segments to meet the growing computing demands of AI. All types of AI technology, research, and applications are satisfied, augmented, and/or accelerated with Azure’s AI-first infrastructure.

Retail and AI

A great example from the industry is retail where Microsoft Azure’s AI-driven cloud infrastructure and toolchain with NVIDIA GPUs are having a significant impact. Learn how Everseen created a seamless shopping experience that benefits their bottom line. With a GPU-accelerated computing platform, customers can quickly browse models and determine the best performing model. And self-checkout enables retailers to provide customers with smoother, faster shopping experiences while increasing revenue and margins. The benefits of an AI-powered cloud infrastructure for retail include:

  • Performance improvements for traditional large-scale data analysis and machine learning processes.
  • Accelerated training of machine learning algorithms. With RAPIDS with NVIDIA GPUs, retailers can utilize larger datasets and process them faster with more accuracy, enabling real-time reaction to shopping trends and saving inventory costs at scale.
  • Forecast accuracy, resulting in cost savings through reduced stock-outs and misplaced inventory.
  • Better and faster customer checkout experience and reduced waiting time in queue.
  • Reducing shrinkage – the loss of inventory due to theft, such as shoplifting or ticket change at self-service checkouts, which costs retailers $62 billion a year, according to the National Retail Federation.

In retail, data-driven solutions require sophisticated deep learning models, models far more sophisticated than those offered by machine learning alone. Deep learning also requires significantly more computing power, which makes optimization through an AI-driven infrastructure and AI toolchain a necessity.

Learn more about purpose-built infrastructure for AI.

AI is everywhere and its application is growing rapidly. An optimized AI-driven infrastructure is essential for the development and deployment of AI applications. Microsoft Azure scale-up and scale-out infrastructure combines the power of NVIDIA GPUs and NVIDIA cloud networking to deliver the right-sized GPU acceleration for AI applications of any scale and for organizations of all sizes.

With a total solution approach that combines the latest GPU architectures and software designed for the most compute-intensive AI training and inference workloads, Microsoft and NVIDIA are paving the way to go beyond supercomputing. exascale AI. Learn how Azure and NVIDIA can help power your AI.


#purposebuilt #cloud #infrastructure

Leave a Comment

Your email address will not be published. Required fields are marked *