High-Performance Architecture
High-Performance Architecture (HPA)
HPA serves as the foundation for modern AI infrastructure by integrating high-performance computing, AI/ML development workflows, and core IT infrastructure components into a single architectural framework designed to meet the intense data demands of AI advanced solutions.
High-performance architecture strategy
AI Factory built on high-performance architecture
High-performance architecture (HPA) is a purpose-built platform designed to process massive volumes of data and solve complex problems at high speed. As the foundation of scalable, production-ready AI infrastructure — what we call the AI Factory — HPA integrates high-performance computing (also referred to as accelerated computing), high-performance networking and high-performance storage, as well as workflow orchestration and infrastructure management to support AI across cloud, on-premises and hybrid environments.
Explore the AI Proving Ground
Learn how WWT has developed and deployed AI solutions on high-performance architectures through our AI Proving Ground. This unrivaled blend of multi-OEM infrastructure, software and cloud connectivity is designed to accelerate the decision-making process when it comes to AI-powered solutions.
Core capabilities of AI infrastructure
High-performance architecture is critical to AI infrastructure
High-performance architecture is essential across every phase of the AI workflow, from model development to deployment. Purpose-built to support fast training, tuning and real-time intelligent interaction, HPA enables enterprises to unlock the full value of their AI investments and build infrastructure that drives growth and agility.
High-performance computing (HPC)
The right amount of combined CPU and GPU processing power is needed to train and run modern AI engines. This combination allows AI models to process large datasets and complex computations quickly and efficiently.
High-performance storage
The ability to reliably store, clean and scan massive amounts of data is required to train AI/ML models. Fast, scalable storage supports real-time access and minimizes delays during training and inference.
High-performance networking
AI/ML applications require extremely high-bandwidth and low-latency network connections. These connections allow rapid data transfer between distributed systems, boosting collaboration and performance.
AI workflow orchestration & infrastructure management
The coordinated management and optimization of AI workloads, resources and infrastructure to ensure efficient, scalable and reliable AI operations across environments.
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High-performance architecture experts
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Phillip Hendrickson
Principal Solutions Architect
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High-Performance Architecture FAQs
Learn more about HPA
Explore common questions about high-performance architecture, technology and implementation to better understand how this solution integrates hardware and software.
High-performance architecture (HPA) integrates high-performance computing, AI/ML workflows, and core IT infrastructure to meet the intense data demands of AI solutions. It supports fast training, tuning, and real-time interactions, enabling enterprises to maximize their AI investments and drive growth.
Scalable machine learning architecture requires strict separation of concerns across data ingestion, feature engineering, training, serving, and monitoring so each layer can evolve and scale independently. It relies on distributed storage and high performance compute, feature stores to ensure training–serving consistency, and automated pipelines with strong versioning, reproducibility, and observability. Ultimately, successful large-scale ML systems are engineered as robust software platforms first, with models treated as continuously deployed and monitored artifacts rather than one-off experiments.
In AI/ML systems, high-performance architecture is a purpose-built platform designed to process massive data volumes and solve complex problems quickly. It combines high-performance computing, networking, and storage to support AI across cloud, on-premises, and hybrid environments.
Choosing the right architecture depends on workload requirements. CPUs are versatile for general tasks, GPUs excel in parallel processing for AI training, TPUs are optimized for specific AI tasks, and hybrid models offer flexibility. Consider factors like data size, task complexity, and integration needs.