AI Readiness and the Role of High-Performance Architecture (HPA)
In this article
We live in an era of rapid technological advancement. A time when generative artificial intelligence (GenAI) is not just a concept from a science fiction novel, but rather, a reality that is reshaping businesses across the globe. In the dynamic landscape of the 21st century, the adoption of GenAI is rapidly changing the business world. It is not only transforming the way we work but also shaping the future of businesses by offering scalable, secure and responsible technological advancements.
Rapid advancements in GenAI have been enabled by what WWT refers to as high-performance architecture or HPA. These architectures serve as the backbone of scalability, speed and security in AI initiatives — each paramount features that pave the way for organizations to accelerate the organizational transformation required for AI success.
HPA plays a critical role in accelerating AI readiness and achieving ROI. Sophisticated systems that allow for faster processing and larger data sets are pivotal. At a high level, HPA combines the elements of:
- High-performance computing (HPC)
- High-performance networking (HPN)
- High-performance storage (HPS)
- AI workflow and orchestration tools
Each area of HPA warrants a discussion on its own. However, these foundational areas should be looked at collectively when planning and building an AI solution. The right architectural design can significantly enhance the effectiveness of AI technologies.
This may imply building a purpose-built architecture in the data center, accessible via cloud services or at the edge. The right amount of combined CPU and GPU processing power, accelerated networking, and an integrated storage platform are all needed to train and run modern AI engines.
Organizations will pursue a hybrid AI strategy in many cases, deploying workloads on-prem, in the cloud and at the edge. Whichever deployment model they choose, the key is enabling new AI solutions and experiences through an HPA. One such example is leveraging Dell Validated Designs for AI. These designs, or HPAs, accelerate AI model deployment and reduce risks with documented and validated solutions that have been designed to help organizations avoid common design and planning pitfalls.
Many companies face barriers to implementing enterprise-wide AI due to technical debt and legacy IT infrastructure. In close collaboration with Dell, WWT is helping customers overcome these barriers by assessing and building the appropriate HPA through design workshops, proofs of concept (POC) in our AI Proving Ground lab environment, and consulting and infrastructure services. To that end, this article outlines the role HPA can play in ensuring AI readiness within your organization.
Need for relevant use cases in AI
AI can enhance many business areas, including in areas such as code development, content and data generation, internal enablement, and support assistants. Examples of how these use cases apply to the business:
- Code generation: Helping developers iterate faster. AI promises to amplify innovation by generating code and reducing technical debt.
- Content and data generation: Leveraging generative models to automatically create customized content, presentations, code and more.
- Internal enablement: New ways of training employees through AI-powered conversational interfaces.
- Support assistants: Training generative models on knowledge bases to create more intelligent and efficient customer/tech support.
Importantly, each organization should prioritize and strategize its AI use cases to effectively drive outcomes.
As organizations investigate infrastructure strategies for supporting a diverse set of AI use cases across the business, it's important to ensure their IT architecture can support the required use cases and desired outcomes. A critical step when planning any HPA involves conducting a prioritization workshop; this preparation can help ensure that AI initiatives are aligned with the organization's strategic goals as well as feasible and impactful in terms of business value.
Scalability, security and the appropriate application of AI are just a few of the elements that must come together for these opportunities to be properly and efficiently utilized. This highlights the pressing need for an effective data architecture and strategy.
Importance of a modern data architecture
Successful AI adoption and integration requires collecting and leveraging the right kind of data. However, great data also entails great responsibilities. Since data hygiene, security and quality are the cornerstones of any successful AI project, their importance cannot be overstated.
This means that to fully utilize AI, a modern, secure data architecture and infrastructure are essential. That said, it's not just about having the right infrastructure; having the right data strategy is equally crucial. Legacy data silos result in messy, inaccurate data that hampers AI outcomes. Modern approaches, such as data fabrics, meshes and lake houses — powered by either on-premises or cloud-native technologies — can aid in data consolidation and provide self-service access.
With an emphasis on data quality, modularity, security and scalability, a proven design is a robust way to kickstart AI projects.
Selecting a flexible architecture with Dell Validated Designs
Dell's Validated Designs for AI are a useful aid in accelerating the AI journey by helping organizations choose the right platform and architecture. But it's not just about speed. Dell Validated Designs for inferencing, model customization, fine-tuning and training come equipped with recommended frameworks for HPAs, offering organizations flexibility as well. A flexible infrastructure is one that can accommodate diverse needs, scale resources, adapt tools and integrate with different data sources as needed. This avoids creating siloed systems for each use case, promoting efficiency and cost-effectiveness.
The modular blueprints from Dell are designed to accelerate GenAI solutions from proof-of-concept to streamlined production deployment. The validated technology stack encompasses NVIDIA-certified systems, software like the NVIDIA AI Enterprise platform, and services for scalable orchestration. Enterprises can leverage Dell's and WWT's partnerships with AI leaders to experiment with different models tailored to their use cases.
A scalable, flexible HPA allows organizations to develop a variety of GenAI solutions tailored to their particular needs, that can revolutionize their industries, and give them a competitive edge.
Composable infrastructure is another concept that aligns with this vision of flexibility. Composability means that organizations can test and leverage different architectures for different use cases, creating a truly customized solution that meets their unique needs.
Final thoughts
Embracing AI requires more than just throwing algorithms at your data. Organizations must:
- Lay a firm foundation: This demands modernizing the data architecture for seamless information flow, ensuring responsible AI development through established ethical principles, and leveraging proven architectural blueprints like HPA.
- Modernizing the data architecture removes bottlenecks and enables efficient AI training and deployment. Adhering to responsible AI principles, like fairness and transparency, builds trust and mitigates risks.
- Finally, HPA-validated designs and blueprints, like Dell Validated Designs for AI, provide a robust and scalable framework for integrating AI solutions, optimizing performance and maximizing impact.
By addressing these three critical aspects, organizations can confidently embark on their AI journey, unlocking AI's power ethically and effectively. Partners like Dell and WWT can help companies at any identify the most valuable AI initiatives and scale them quickly — wherever you are in your AI journey.