Ever wonder why your AI project feels like it’s stuck spinning its wheels? Chances are, your infrastructure isn’t keeping up. AI workloads are beasts—hungry for compute power, storage speed, and seamless coordination.
This isn’t just about tossing cash at fancy hardware; it’s about designing a setup that’s smart, scalable, and ready for the future.
In this installment of our AI infrastructure series (catch up on networking if you missed it), we’re diving into the heart of the matter: compute and storage.
Selecting the Best Compute for AI
AI is not your average IT workload. Where traditional apps might jog along happily on a CPU (central processing unit), AI demands a sprinter—or better yet, a whole relay team.
Choosing the best compute depends on what you’re asking your AI to do: are you training massive models, running real-time predictions, or crunching scientific simulations?
For basic AI applications—like lightweight inference workloads—CPUs can still get the job done. Think of a CPU as a skilled chef working solo in a kitchen: it’s versatile, precise, and great at handling a sequence of tasks one-by-one, from chopping to plating. That sequential nature makes CPUs ideal for general-purpose computing or AI jobs that don’t demand heavy lifting. But when it comes to training complex neural networks, CPUs hit a wall—they just don’t have the horsepower for massive parallel processing.
For GPUs (graphics processing units). Picture a GPU as a factory floor packed with workers, each tackling a small piece of the puzzle simultaneously. With thousands of simple cores arranged in a grid, GPUs are built for high-performance computing tasks that require raw computational power. This parallel power makes them perfect for crunching through the enormous datasets at the heart of AI model training.
GPUs, however, are not the only compute accelerators available. Organizations with specific AI needs may want to explore specialized processors such as application-specific integrated circuits (ASICs).
Think of ASICs as custom-built race cars, tuned for one specific track. They are specially-designed computer chips that can be custom-programmed for optimal performance of specific tasks—things like image processing or speech recognition. Multiple circuits are combined on a single chip, making ASICs compact, reliable, and high performing. For AI and machine learning operations, ASICs can accelerate processing, enabling you to train AI models on large data sets and execute inference tasks faster and more efficiently. While ASICs offer superior efficiency, they are not as flexible as GPUs, making them best suitable for highly specialized AI applications.
NVIDIA’s Heavy Hitters: H100, H200, DGX, and HGX
Some organizations are ready to go big on AI. If you’re looking for a Ferrari, NVIDIA has you covered. NVIDIA is the industry leader when it comes to enterprise AI infrastructure with several offerings, including H100/H200, DGX, and HGX.
The NVIDIA H100 and H200 NVL GPUs represent the latest advancements in AI acceleration. They are designed to handle the growing complexity of large language models (LLMs) and generative AI applications. The H100 GPU introduced Transformer Engine acceleration, significantly improving model training efficiency, while the H200 NVL builds on this by incorporating higher-bandwidth memory (HBM3e), enabling faster data movement and better performance for AI inference.
DGX stands for Deep Learning GPU eXtension/Accelerator. DGX is NVIDIA’s platform for enterprise AI. It provides a full-stack environment, integrating hardware and software specifically designed for AI development. The platform offers high performance computing power and scalability, which are needed for training AI models and supporting AI use cases such as research, medical diagnostics, fraud detection, and others. DGX is exclusively an NVIDIA make-model product.
NVIDIA’s HGX platform is designed for hyperscale environments, such as data centers and large cloud providers. It allows multiple GPUs to work in tandem, employs high-bandwidth interconnect technologies between GPUs, and offers a scalable, modular architecture so organizations can build customized environments for complex AI workloads that demand high-performance computing power. HGX is well suited for training large neural networks, supporting scientific modeling, and data analytics among other use cases.
(For more information on NVIDIA platforms and other key AI terms and acronyms, check out this.)
Managing AI Compute Clusters: Orchestration and Scheduling
Having the right hardware is only half the battle. Managing an AI compute cluster is like conducting an orchestra—every GPU needs to hit its note, or you’re left with a cacophony of wasted resources.
To address this, AI workloads are often managed through cluster orchestration tools. NVIDIA Base Command Manager (BCM) is an enterprise-grade solution designed for managing AI and HPC clusters, enabling seamless provisioning and monitoring of AI workloads. Open-source alternatives like Canonical MAAS and Warewulf offer similar capabilities, allowing organizations to automate bare metal provisioning.
On the workload scheduling side, Kubernetes has emerged as the standard for AI/ML containerized workloads. Kubernetes provides dynamic resource allocation, ensuring AI workloads can scale efficiently across multiple GPUs. For traditional HPC environments, however, SLURM (Simple Linux Utility for Resource Management) remains a popular choice, especially for batch-processing AI jobs that require large-scale parallelism. Other solutions, such as Run:ai, integrate AI workload management on Kubernetes, enabling intelligent GPU resource sharing among multiple users.
Facilities Impact: More Power, More Cooling
AI doesn’t just eat data—it guzzles power and spits out heat, exceeding the cooling capacity of traditional data centers. As AI accelerators like NVIDIA H100/H200 and AMD MI300X push power consumption beyond 700 watts per GPU, organizations must adopt advanced cooling solutions to maintain efficiency.
Traditional air cooling is no longer sufficient for dense AI clusters. Many enterprises are transitioning to direct-to-chip liquid cooling and immersion cooling, which significantly improve thermal management and reduce energy costs. AI-optimized data centers are also deploying high-density Power Distribution Units (PDUs) to handle the increased wattage demands. Proper power and cooling strategies are essential to prevent thermal throttling and maximize the performance of AI infrastructure.
Training vs. Inferencing
Data is the lifeblood that fuels both training and inferencing processes. Each phase, however, comes with its own unique demands and challenges.
Training is like teaching a kid to read—slow, data-heavy, and full of trial and error. Massive datasets are fed into the model to teach it how to spot patterns, make predictions, or classify information. This phase is a computational beast, demanding vast amounts of data and efficient storage systems for seamless access. Challenges include wrangling data quality, ensuring diverse datasets to avoid bias, and managing sheer volume without choking the pipeline.
In contrast, inferencing is the trained model putting its skills to work—like a kid reading solo, now quick, precise, and sharp under pressure. Once trained, the kid can zip through a story, making fast, accurate calls based on what they’ve learned. Data needs here are lighter than in training, but the focus shifts to latency, scalability, and rock-solid performance across workloads. Inferencing systems have to deliver snappy responses—think real-time apps where delays aren’t an option—while keeping accuracy and reliability on point.
Tackling the Storage Question
Storage for AI isn’t just a big general purpose data pool—it’s about speed, scale, and keeping up with your compute. A sluggish storage system is like a sports car with a clogged fuel line—all that power, nowhere to go.
AI systems require a lot of data to be accessible. This requires strong data storage and management solutions that can handle large data volumes, ensure data quality, and offer dependable, quick access. When it comes to storage, scalability is fundamental—your infrastructure must be able to scale as the volume of data grows.
Figuring out what kind of storage you need depends on many factors, including the level of AI your organization plans to use and whether you need to make real-time decisions. For example, a FinTech company that uses AI systems for real-time trading decisions may have much more robust storage requirements than companies with less real-time requirements that can leverage denser and most cost-effective solutions.
Businesses need to factor in how much AI data applications will generate. AI applications make better decisions when exposed to more data. As databases grow over time, companies need to monitor capacity and plan for expansion.
The choice of AI storage depends on workload requirements. Distributed storage systems like Ceph and Hadoop Distributed File System (HDFS) are perfect for sprawling AI data sets and are commonly used for big data AI applications, offering scalability and fault tolerance. For high-performance AI training, parallel file systems such as Lustre, IBM Spectrum Scale (GPFS), and WekaFS are preferred. These file systems support GPUDirect Storage (GDS), allowing direct data transfers between storage and GPUs, minimizing bottlenecks.
For cloud-native AI workloads, Kubernetes-based storage solutions such as Portworx (Pure Storage) and NetApp Astra Trident weave storage into Kubernetes, keeping containerized AI workflows smooth. Meanwhile, traditional storage vendors like NetApp, Pure Storage, and VAST Data provide AI-optimized storage platforms that support multi-protocol access, enabling seamless data sharing across AI clusters.
Key things to consider regarding your storage architecture:
- Use high-performance storage devices: your storage solution should support flash technology, addressing conventional SSD shortcomings. This ensures fast access and extended flash lifetimes, meeting the high-performance requirements of data-intensive research tasks.
- Make sure you have a unified network: the storage architecture should feature a unified network, consolidating client and administrative communication. This streamlined approach enhances internal concurrency, supporting both IPv4 and IPv6 at Ethernet speeds up to 100 Gb/s.
- Employ a symmetric distributed storage operating system: a symmetric distributed storage operating system ensures load balancing, distributing workloads evenly among processing and I/O resources. This approach enhances overall system performance and efficiency.
- Simplify administration: storage systems design should offer administrative simplicity by autonomously performing routine tasks, self-tuning, and self-healing in case of component failure. Regular status reports to centralized management tools enhance visibility, utilizing historical data fingerprints for proactive issue detection and remediation.
Managing the Data Pipeline
Picture your data pipeline as a bustling restaurant kitchen. Raw data (ingredients) needs prepping, cooking, and serving—fast.
Effective AI workflows hinge on strong data management and streamlined data pipelines to ensure the right data is available in the right format at the right time.
Data preprocessing and feature engineering are critical first steps, as AI models rely on high-quality, structured, and labeled data to perform accurately. This often involves ETL (Extract, Transform, Load) pipelines tailored for AI workflows, which clean, normalize, and transform raw data into actionable insights.
Another key consideration is data gravity—the concept that data becomes increasingly difficult to move as it grows in volume. To combat latency and inefficiency, AI processing should be brought as close to data storage as possible. Technologies like GPUDirect Storage (GDS) and RDMA (Remote Direct Memory Access) play a pivotal role here, enabling faster data transfer between storage and GPUs, thereby accelerating AI training and inferencing. By addressing these challenges, organizations can build efficient data pipelines that empower their AI systems to deliver optimal performance.
Putting it Together
Building an AI infrastructure is not as simple as deploying a few GPUs and storage arrays. The choice of compute and storage components depends on factors such as workload complexity, data throughput requirements, and budget.
For AI to deliver on its promises, organizations must carefully evaluate their hardware and software strategy, whether they are training large-scale deep learning models, deploying AI inference workloads, or running high-performance computing (HPC) applications.
When it comes to compute and storage for AI, there are a lot of options. What makes sense for your organization depends on a variety of factors. The key is first to have a clear understanding of your AI objectives and strategy, and then build from there.
For help with any stage of your AI journey, check out ePlus AI Advanced Services.