Introduction to AI Infrastructure

According to the Artificial Intelligence Index Report 2024, twice as many new large language models were released worldwide in 2023 than the year before.¹ You can expect this trend not only to continue but also to accelerate. 

As a result, the demand for robust and scalable AI infrastructure has never been higher than it is today—but that too is only going to increase as AI continues to evolve and reshape industries.

Since so much success with AI depends on infrastructure, I’ve written this series on it. In this introductory post, we'll explore infrastructure challenges with AI, the key components that make up AI infrastructure, and discuss the importance of both on-premises and cloud environments.

Challenges and Considerations for Building AI Infrastructure

Building AI infrastructure is a complex task. As you move into this phase, here are some key challenges and important considerations to ensure scalability, efficiency, and optimal performance:

• Scalability: Ensuring that your infrastructure can scale to meet the growing demands of your AI workloads is crucial. This involves careful planning of your compute, network, and storage resources. In distributed AI workloads, GPU network sensitivity to packet loss and out of order packets can be a crucial factor that can impact performance. Automation needs to be part of your AI cluster, ensuring that you can scale or re-purpose resources on-demand when required.
• Cost: Balancing CapEx and OpEx is key to maintaining financial viability, particularly when investing in high-performance hardware like GPUs and specialized storage solutions. Key areas you should focus on are resource control, sharing, and optimization within your infrastructure used for AI. This will ensure you get the most out of your infrastructure investment.
• Integration: Seamless integration of the various components (compute, network, storage, ML software) is essential for achieving optimal performance and efficiency. These components must work harmoniously together to support the entire AI lifecycle.
• Maintenance and Management: Ongoing management, including updates, security patches, and performance monitoring, is vital to keeping your AI infrastructure running smoothly.

Overview of AI Infrastructure Components

Building AI infrastructure involves several critical components, each contributing to the overall performance and scalability of your AI workloads. Let's break down these components:

1. Compute
• CPUs (Central Processing Units): CPUs play a very important role in your AI Infrastructure and are the backbone of any computing environment. CPUs handle general-purpose processing tasks related to the operating system and tasks related to running software that supports your AI infrastructure. Utilizing CPUs that are purpose built for AI infrastructure is key. Intel and AMD are the leading providers in this space, offering a range of options tailored for AI and ML workloads.
• GPUs (Graphics Processing Units): GPUs are the workhorses of AI, designed to handle the parallel processing required for tasks like training neural networks and AI inferencing. NVIDIA’s DGX systems are industry leaders, offering unparalleled performance for AI workloads
• TPUs (Tensor Processing Units): TPUs, developed by Google, are specialized processors optimized for AI workloads, particularly those using TensorFlow. They are available through Google Cloud.
  
2. Network
• High Throughput / Low latency Networking: During both AI Training and Inferencing, multiple GPUs across multiple compute nodes are utilized in order to speed up the processing of large datasets and complex models. This enables faster training times and quicker responses to user queries. This is called parallel compute and having a low latency, high throughput network is key. These network connections are specialized towards HPC and AI clusters and often use InfiniBand or Ethernet for transport.
• RDMA (Remote Direct Memory Access): RDMA is a specification that allows direct memory access (DMA) from one computer to another. RDMA bypasses the CPU to reduce latency and increase throughput. This specification is critical for parallel computing because it allows data to be written to the memory of a remote system’s GPU. There are many protocols that support RDMA transport, including InfiniBand and RDMA Over Converged Ethernet (ROCE). Utilizing a properly designed, purpose-built network that supports these protocols is key.
• High-Speed Interconnects: Technologies such as AMD's Infinity Fabric and NVLink developed by NVIDIA enable GPUs within a compute node to communicate with one another at high speeds with low latency. This in turn, provides support for building large-scale GPU clusters with multiple interconnected GPUs, enabling for massive parallel processing capabilities.

3. Storage
• HDDs (Hard Disk Drives): Traditional storage solutions that offer large capacity at a lower cost.  While these drives are considered legacy and do not offer the same speeds as their SSD counterparts, they do have their place in AI Infrastructure. These cost-effective drives can be used for storing massive amounts of data when speed is not a concern. They can be used either for data lakes or for archival and backup purposes.
• SSDs (Solid-State Drives): These drives are faster and more reliable than HDDs due to their lack of moving parts within the drive itself. SSDs are ideal for active datasets which are frequently accessed and used for model training. They can also be used for other high-performance compute scenarios where data needs to be accessed and processed quickly.
• NVMe (Non-Volatile Memory Express): NVMe drives offer even faster data transfer speeds and lower latency (as compared to SSDs), making them perfect for demanding AI applications. These drives leverage PCIe interfaces internally within the compute node. These drives can be used for real-time AI inferencing and large-scale model training with large, complex data sets.
• Object Storage: Solutions like S3 (Amazon Simple Storage Service) and Swift (from OpenStack) provide scalable storage for unstructured data, essential for handling large datasets. Data is stored as objects with unique identifiers and metadata, making it easily searchable and accessible. This storage type is ideal for data lakes and model artifacts. They’re also ideal for collaboration and data sharing amongst teams working on AI projects.
  
4. Kubernetes Cluster
• Kubernetes: This open-source container orchestration platform is used to automate the deployment, scaling, and management of containerized workloads and applications. In AI infrastructure, Kubernetes plays a crucial role in managing and orchestrating AI workloads across distributed environments.
  
5. Cluster Management
• NVIDIA Base Command Manager (BCM): BCM is a powerful tool for managing AI clusters, providing features like job scheduling, resource management, and monitoring. BCM also can provision the physical nodes through assigning node categories allowing you to rapidly scale or repurpose your AI cluster based on demand. It’s designed to work seamlessly with NVIDIA’s DGX systems, and other OEM servers with NVIDIA GPUs, making it easier to deploy and manage AI workloads and AI clusters at scale.
• Kubernetes Cluster Managers: Tools like Rancher, OpenShift, and Kubespray offer alternatives to BCM, each with its own strengths and use cases, depending on the specific requirements of your AI infrastructure.
  
6. ML Software Stack
• TensorFlow, PyTorch, and Jupyter Notebook: These are some of the most popular frameworks and tools used for developing and running AI models. Each has its own advantages, and the choice of software stack will depend on your specific use case, performance needs, and ease of integration with your infrastructure.

Now that we’ve reviewed the infrastructure components, let’s discuss where to build it.

On-Prem and Cloud Infrastructure for AI Workloads

The choice between on-premises and cloud infrastructure is a critical decision that impacts cost, performance, scalability, and security. Let’s take a brief look at each of them.

• On-Prem Infrastructure: Ideal for organizations that require full control over their AI infrastructure, offering benefits like reduced latency, better security, and potential cost savings in the long run.
  
  On-premises solutions are particularly suited for industries with strict regulatory requirements or those dealing with sensitive data.
  

• Cloud Infrastructure: Offers unmatched flexibility and scalability, making it easier to handle varying workloads without the need for significant upfront investment in hardware.
  
  Cloud providers like AWS, Google Cloud, and Azure offer robust AI platforms that include compute, storage, and network resources optimized for AI workloads.
  

• A hybrid approach: Combining the best of both on-prem and cloud environments can provide the most effective solution, balancing control, cost, and scalability.

The best approach for your organization depends on a number of factors. For more information, check out our post To Build or to Consume? Selecting the Right Option for Your AI Initiative.

In the upcoming posts in this series, we’ll dive deeper into each of these components, providing detailed insights and practical advice on building optimized AI infrastructure from scratch.

For help with any stage of your AI journey, ePlus offers a comprehensive set of services. Check out ePlus AI Ignite for more information.

1. Artificial Intelligence Index Report 2024, Stanford University. Accessed September 10, 2024. https://aiindex.stanford.edu/wp-content/uploads/2024/05/HAI_AI-Index-Report-2024.pdf