High-performance architecture is the foundation of AI infrastructure

High-performance architecture (HPA) is a platform and environment designed and purpose-built to process massive amounts of data and solve complex problems at very high speeds. HPA serves as the foundation for modern AI infrastructure, enabling the compute power and scalability enterprises need to develop and deploy AI at scale. 

A high-performance architecture includes high-performance computing (also referred to as accelerated computing), high-performance networking and high-performance storage.

Additional characteristics of an HPA can include:

  • Parallel processing: Clusters of high-powered processors operating on similar workloads that can be spread across multiple compute nodes, executed simultaneously, and the outputs consolidated for faster completion.
  • Purpose-built and optimized for high performance: All components in an HPA are designed for high speed, high throughput and low latency to maximize throughput. 
  • Dynamic architecture: Compute, storage and networking can grow and shrink to match workload scale demands.

AI infrastructure requires a purpose-built, high-performance architecture that supports rapid innovation and delivers scalable, real-time intelligence. Learn how WWT has developed and deployed AI solutions on high-performance architectures. 

Explore AI solutions in WWT's AI Proving Ground

High-performance architecture is critical to each phase of the AI workflow, from model development through deployment. Without the right foundation, enterprises will struggle to realize the full value of investments in AI applications.

High-performance computing (HPC)

The right amount of combined CPU and GPU processing power is needed to train and run modern AI engines. 

High-performance storage

The ability to reliability store, clean and scan massive amounts of data is required to train AI/ML models. 

High-performance networking

AI/ML applications require extremely high-bandwidth and low-latency network connections. 

Automation, orchestration, software and applications

HPA requires the right mix of data science tools and infrastructure optimization.

Strategy and governance

Data must be protected and readily accessible to systems and users.

Talent and skills

The right mix of experts is needed to build AI models and maintain HPA infrastructure. 

High-performance architecture is critical to AI infrastructure

AI solutions require purpose-built architectures that allow for fast model development, training, tuning and real-time intelligent interaction. This makes HPA a core component of any robust AI infrastructure strategy, particularly for enterprises building AI for business infrastructure that supports growth and agility.

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Virtual events

Check out these upcoming and on demand events.

Building AI-Ready Data Centers with Cisco

The rapid evolution of AI technologies is transforming enterprise operations and reshaping the data center landscape. As businesses look to harness AI's full potential, critical questions around data center performance and capacity arise. Join WWT Senior Practice Manager for High-Performance Architecture Jeff Fonke as he talks with Cisco Fellow and Chief Architect of Global Solutions Engineering Matt Gillies as they discuss how you can unify networking, compute, security and observability to deliver AI-ready data centers.​ Jeff and Matt also talk about AI Pods that can accelerate AI model deployment with ​pre-validated, full stack systems; and other Cisco innovations making an impact on the AI landscape. ​
Webinar
•Dec 5, 2024 • 9am

Kubernetes' Key Role in Driving Enterprise AI Success | Research

AI development often involves diverse tools and frameworks that necessitate a flexible and portable infrastructure. Learn from Technical Solutions Architect Steve Younger about why the Kubernetes platform is inherently designed for flexibility and portability, making it highly suitable for managing AI workloads across different environments.
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Sep 11, 2024 • 9am

Modernizing the Data Center to Capitalize on AI with Cisco at Cisco Live! | Beyond the Booth

Generative AI may sit atop the executive agenda, but few organizations are fully prepared to harness the full potential of AI and AI-powered technologies. While at Cisco Live, we caught up with Cisco's David Jensen and WWT's Justin van Schaik to talk about the need to modernize and scale enterprise data centers for the surging growth in AI. We also talk with David and Justin about managing AI environments and workloads and how Cisco Nexus HyperFabric AI clusters make it easy for enterprise customers to build infrastructure to run generative AI models.
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Jun 20, 2024 • 9am

Go From AI-Possible to AI-Proven with Dell Validated Designs | Partner Spotlight

AI strategies are taking flight across all industries, but very few are moving at the same speed. Whether you're a slow mover or fast learner, learn how using Dell's Validated Designs for Generative AI can help you get where you need to go faster and with less risk.
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May 16, 2024 • 9am

Move Beyond AI Readiness: Key Technology Decisions to Accelerate AI Growth | Research

The incredible potential of AI to drive generational transformation has the C-suite scrambling for ways to best position their organizations for success. But readiness is just half the battle — real business and technology decisions must be made to drive success past the finish line and beyond. Join WWT AI experts Tim Brooks and Brent Collins as they discuss the key technology decisions executives must guide for AI transformation to come to fruition, such as buying or building AI models, cloud vs on-prem deployment models, data architectures and management strategies, security and building IT infrastructure that can handle AI workloads.
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Apr 11, 2024 • 9am

Ready to get started?

High Performance Computing (HPC) Discovery & Alignment Workshop

High-performance computing (HPC) is the ability to process data and perform complex calculations at high speeds delivering performance and scale. In today's markets, a successful HPC cluster can be a formidable competitive advantage. And many are turning to these tools to stay competitive. That said, these systems are inherently very complex and must be built, deployed and managed properly to realize their full potential. Improper implementation can hinder your enterprise's ability to meet implementation deadlines, quickly identify and resolve problems, perform updates and maintenance and more.
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Components of Compute

A server is a crucial piece of hardware in every organization's data center. Its primary role is to support the success of an organization's web-tier, mid-tier, database, and AI application stack from a networking, processing, and storage standpoint regardless if the application is deployed within a natively installed operating system, in a virtual machine, or as a cloud-native or edge-native application. The server components that make up this hardware system can vary depending on the specific needs and requirements of the organization. The main components commonly found in a server include CPU (central processing unit), Memory, Network Interface Cards, Storage Devices, and in some cases accelerators like DPUs (data processing unit) and GPUs (graphical processing unit). In this Learning Path engineers can learn about the latest solutions on the market today that they can integrate into their solutions to help drive their business forward most effectively and efficiently.
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