Constructing AI Infrastructure: 5 Essential Steps and Strategies

In recent years, artificial intelligence (AI) has revolutionized the business world, creating a demand for AI capabilities that often surpass existing organizational strategies. This has led companies to seek effective ways to integrate AI into their operations. As companies are increasingly automating departments with AI and integrating new AI features into their applications, it is important to maximize AI’s potential with a well-planned, scalable IT infrastructure.

1. Current Infrastructure Assessment

A strategic approach to cloud and overall IT infrastructure is imperative. Changes, especially core IT infrastructure, require a comprehensive evaluation of the business model and anticipated workloads, perhaps years in advance, highlighting the critical importance of meticulous planning in the era of AI. This assessment should encompass hardware (e.g., servers, storage, networks), software (e.g., databases, application platforms), and existing data management practices.

Several concepts and frameworks traditionally assist organizations in assessing their IT infrastructure. Utilizing such methodologies can offer structured approaches for evaluating the effectiveness, efficiency, security, and alignment of IT systems with business objectives:

• ITIL (Information Technology Infrastructure Library): ITIL offers a detailed framework for IT service management to align IT services with business needs, covering service design to improvement. It encourages a flexible approach to IT management, with ITIL 4 providing significant advancements.
• COBIT (Control Objectives for Information and related Technology): COBIT provides a comprehensive framework for enterprise IT management and governance, ensuring alignment with business objectives, risk management, and performance optimization.

2. Computational Power and High-performance Processors

AI, and deep learning in particular, demands processors with significant computational capabilities, such as Graphics Processing Units (GPUs), Tensor Processing Units (TPUs), or Field-Programmable Gate Arrays (FPGAs).

When selecting a GPU for AI infrastructure, it’s important to consider the specific requirements of your workload. This includes determining whether the focus is on training or inference, assessing the size and complexity of your models, considering budget constraints, and evaluating the software ecosystem.

NVIDIA’s GPUs, especially the A100, H100, or H200, are highly favored in the industry for their performance, comprehensive software support, and specialized AI acceleration features. Nevertheless, AMD and Intel are becoming increasingly viable alternatives, particularly in scenarios where their unique features or cost-effectiveness present clear benefits.

Another critical consideration is the ability to scale resources according to workload demands. This scalability is essential for managing costs and maintaining efficiency throughout the various AI model development and deployment stages. Amazon uses AI to optimize various aspects of its operations, including inventory management, personalized recommendations, and logistics. During peak shopping periods like Black Friday or Cyber Monday, the demand on Amazon’s systems surges dramatically. To handle this, Amazon leverages cloud computing platforms that allow for dynamic scaling of resources.

3. High-volume Storage and Management

AI systems require the capability to store and manage substantial volumes of data. This necessitates fast, scalable storage solutions, such as object storage for unstructured data and high-performance databases for structured data. Fast and reliable access to this data is imperative for the effective training of AI models.

Ceph, as a storage solution, exemplifies flexibility and efficiency in handling large data volumes. It ensures compatibility with existing applications and facilitates integration with cloud platforms, making it a cost-effective solution. An alternative way to provide mass data storage and high capacity efficiently is using NVMe over Fabrics (NVMe-oF).

NVMe over Fabrics (NVMe-oF) can significantly aid in building powerful and cost-effective data storage systems, especially where high performance and scalability are required. NVMe-oF leverages NVMe SSD advantages like low latency and high data transfer speeds over network connections. Another advantage is the easy scaling of storage systems by adding more NVMe devices to the network without performance loss, allowing organizations to meet growing data storage needs without a complete infrastructure overhaul.

4. Software and Cloud Platform Providers

Choosing the right cloud platform or vendor is a critical decision for AI infrastructure. While most cloud platforms are capable of supporting AI workloads, the primary consideration should be compatibility with the processor selected for your system. However, for an infrastructure to be truly effective, this alone is not sufficient.

The expertise of the AI infrastructure team is vital for achieving optimal performance. Despite the prevalence of cloud virtualization, it may not always be the best for AI systems. A hybrid model of cloud, virtualization, and bare metal, as shown in one case, can effectively meet deep learning’s demands, blending computing power distribution with high-performance bare metal access.

JPMorgan Chase adopted a hybrid cloud infrastructure that combines cloud, virtualization, and bare metal solutions. This hybrid model allows JPMorgan Chase to leverage the flexibility of cloud and virtualization for scalability and cost-effectiveness while also utilizing the power of bare metal servers to handle compute-intensive AI tasks.

The infrastructure must be flexible enough to adapt to evolving AI demands, enabling the incorporation of new technologies and the adjustment or expansion of resources with minimal disruption. Technologies such as OpenStack for virtualization and Kubernetes for containerization play a vital role in managing AI applications. They simplify the deployment, scaling, and operation of AI workloads across varied environments, enhancing the infrastructure’s agility and responsiveness to changing needs.

5. Energy Efficiency and Consumption

Incorporating AI into IT infrastructure boosts data processing and algorithm execution capabilities but raises energy consumption concerns, especially for deep learning models that need significant computational power. The energy part is the most challenging aspect because the conventional strategy for improving energy efficiency involves redistributing loads and powering down unused capacities.

Thus, it is advisable to focus on balancing performance and identifying and managing the components of the infrastructure that consume the most energy. In AI infrastructure, these are typically GPUs, FPGAs, and other hardware components that require continuous cooling or heat dissipation.

Significant savings can still be achieved by improving the efficiency of the cooling systems. For instance, data centers in Iceland, like Borealis or atNorth, illustrate an effective approach to energy management. Leveraging Iceland’s cool climate and abundant renewable energy sources, these data centers utilize natural cooling and geothermal energy, significantly reducing the need for artificial cooling and hence lowering the overall energy consumption of AI infrastructures.

Exploring energy-efficient GPUs and TPUs, and optimizing AI algorithms through model pruning and quantization, are key for reducing energy use while maintaining performance. Furthermore, adopting green data center technologies, leveraging virtualization and cloud computing, and employing dynamic scaling and AI-driven resource management improve energy efficiency in AI operations by tailoring resource use to demand.

In conclusion, for organizations aiming to harness innovation and gain a competitive edge, transforming IT infrastructure to be AI-ready is essential.