What are GPUs and TPUs, and how do they influence the design of hardware infrastructure for AI systems?

Artificial Intelligence (AI) has an insatiable appetite for computing power. Recent years have seen a surge in AI development that pushed computing demands to new heights. To meet this demand, engineers rely on specialized hardware. Two key players are GPUs (Graphics Processing Units) and TPUs (Tensor Processing Units) – both are critical in modern AI hardware infrastructure and system architecture. But what exactly are GPUs and TPUs, and how do they shape the design of the hardware systems that run today’s AI and deep learning workloads? This guide breaks down these concepts in simple terms, with real-world examples and easy explanations. By the end, you’ll understand the roles of GPUs and TPUs in machine learning acceleration and how they influence system design for AI. (For a deeper dive into AI basics, check out our Grokking Modern AI Fundamentals course.)

What is a GPU?

A GPU (Graphics Processing Unit) is a special processor originally created to render images and video for computer graphics. Think of how video games or 3D animations need quick image updates – GPUs were made for that. Unlike a general-purpose CPU (Central Processing Unit) that handles one thing at a time (sequentially), a GPU can handle many operations in parallel. In practical terms, a GPU contains thousands of smaller cores that work simultaneously on different parts of a problem. This makes GPUs very fast at performing repetitive mathematical tasks on large datasets, such as the matrix multiplications at the heart of neural networks.

Over time, GPUs evolved from just graphics accelerators into versatile processors for scientific and AI computing. Around the late 2000s and early 2010s, researchers discovered that those same parallel processing capabilities could dramatically speed up AI algorithms. In fact, GPUs have become indispensable for training and running deep learning models because they can handle massive amounts of data and calculations in parallel. This parallelism is exactly what complex AI tasks like image recognition or language modeling need. By using GPUs, tasks that would take a CPU days or weeks can be completed in hours. Today’s popular AI frameworks (like TensorFlow and PyTorch) automatically use GPUs to accelerate machine learning workloads, making advanced AI accessible to researchers and developers worldwide.

Real-world example: OpenAI’s famous GPT-3 language model was trained on a cluster containing thousands of NVIDIA GPUs working together. In one case, a supercomputer built by Microsoft and OpenAI had over 10,000 GPUs linked with ultra-fast networking to handle training of large AI models. This shows how modern AI system architecture often revolves around scaling out with many GPUs in parallel to achieve the needed performance.

What is a TPU?

A TPU (Tensor Processing Unit) is another type of processor – but it’s one that was designed from the ground up for AI by Google. In contrast to GPUs (which started as graphics chips and were later adapted for AI), TPUs were built specifically to accelerate machine learning workloads. The name “Tensor” comes from the fact that these chips focus on tensor operations – the matrix and vector calculations fundamental to neural networks.

Google introduced TPUs in 2016 as purpose-built chips (also known as ASICs – Application-Specific Integrated Circuits) for its machine learning needs. What makes TPUs unique is their custom architecture optimized for the types of math in AI. They have many units dedicated to multiplying matrices (a common operation in deep learning). This means TPUs can perform certain neural network computations extremely quickly and efficiently, often with lower power usage for those tasks compared to general GPUs. In simple terms, a TPU is like a highly specialized worker that does one job (AI math) very well, whereas a GPU is more like a jack-of-all-trades for parallel tasks.

TPUs are used heavily inside Google’s data centers to power products like Translate, Search, and Photos, and they’re also available to outside developers via Google Cloud. In fact, you can rent Cloud TPU slices online to speed up your own AI projects. Because they’re custom Google hardware, TPUs are mainly accessible through Google’s cloud services rather than as physical cards you buy off the shelf. Companies choose TPUs for deep learning workloads that benefit from maximum throughput – for example, training large language models or running complex inference for many users. Google reports that TPUs have enabled significant leaps in training speed; tasks that once took a day on a fleet of GPUs can run in mere hours on a TPU pod. We’ll talk about “TPU pods” next, as they illustrate how TPUs influence AI infrastructure design.

GPUs vs. TPUs: Key Differences and Roles

Both GPUs and TPUs are hardware accelerators that make AI computations faster, but they have different origins and design philosophies. Here’s a quick comparison of their key differences:

• Origin & Purpose: GPUs were originally made for graphics (developed by companies like NVIDIA and AMD) and later repurposed for general computing and AI tasks. TPUs, on the other hand, were created by Google exclusively for AI from the start. In other words, GPUs evolved into AI workhorses, while TPUs were born as AI specialists.
• Architecture: A GPU contains hundreds or thousands of processing cores that are highly flexible. They excel at parallel tasks and can handle a wide range of algorithms. A TPU contains a high number of specific units (e.g. matrix multiplication units) optimized for tensor operations. This specialization means TPUs can execute certain neural network computations even faster and more efficiently than GPUs. However, TPUs are less suited for non-AI tasks.
• Ecosystem & Availability: GPUs are widely available in consumer devices, workstations, and cloud platforms. Developers have built a rich ecosystem of software (like CUDA libraries and various deep learning frameworks) around GPUs. TPUs, by contrast, are primarily available through Google Cloud. They work with frameworks like TensorFlow (and now PyTorch) but are not as universally accessible as GPUs. If you need a lot of raw AI power and use Google’s platform, TPUs are an option; for most other cases, GPUs are the go-to solution.
• Use Cases: GPUs are very versatile – they can train models, run inference (making predictions), and even handle other parallel tasks like video processing or scientific simulations. They are commonly used across industry and research for tasks from image classification to gaming and beyond. TPUs are more focused – they shine in large-scale training and inference tasks, especially in Google’s own applications. For example, Google uses TPU clusters to train huge models for translation and search ranking. Organizations outside Google might use TPUs through the cloud to accelerate specific projects (like very large deep learning models), whereas GPUs might be used for both big and small projects due to their flexibility.

In summary, GPUs are the all-purpose workhorses of AI acceleration, while TPUs are more like a racehorse – less flexible, but extremely fast for the jobs they’re trained to do. Depending on the project, an AI system architect might choose one or the other (or even both) to balance speed, cost, and convenience.

How GPUs and TPUs Shape AI Hardware Infrastructure

These accelerators don’t just make individual programs faster – they have fundamentally changed AI system architecture and hardware design. Here are some key ways GPUs and TPUs influence modern AI infrastructure:

• High-Performance Clusters: Because cutting-edge AI often requires many GPUs or TPUs working in parallel, companies design specialized clusters and supercomputers around them. For instance, Google connects its TPUs into “TPU pods.” A TPU pod is essentially a supercomputer built of 64 or more TPUs woven together with a high-speed network, yielding up to 11.5 petaflops (quadrillions of operations per second) of processing power as a single system. This allows training of a single large model to be split across dozens of TPUs at once. Similarly, organizations like OpenAI build GPU supercomputers – one such system had 10,000 GPUs linked with 400 Gbps networking per server to handle enormous AI models. These examples show that system design for AI often means scaling out with many accelerators, and ensuring they can communicate quickly with each other and with CPUs. The need for ultra-fast interconnects (like NVLink for GPUs or Google’s proprietary TPU interconnect) and massive parallelism is a direct result of using GPUs/TPUs.

• Power and Cooling Considerations: GPUs and TPUs are power-hungry. A server with 8 high-end GPUs, for example, draws a lot of electricity and generates substantial heat. Data center designers must account for this with robust power supplies and cooling systems (sometimes even liquid cooling for dense GPU/TPU racks). This influences facility design – high-density deployments with these accelerators need advanced cooling and energy management. In fact, efficiency gains in newer GPU/TPU models are as crucial as raw speed, since cooler, less power-intensive chips allow packing more into a data center. Modern AI infrastructure strives for performance and efficiency, often by using the latest hardware that can do more work per watt.

• Cloud Infrastructure: Many organizations choose to access GPUs and TPUs through cloud providers instead of owning the hardware. Cloud platforms (Google Cloud, AWS, Azure, etc.) offer rentable GPU and TPU instances because not everyone can build their own AI supercomputer. This cloud-based approach to AI infrastructure is popular due to its scalability and flexibility. For example, a company can spin up dozens of GPU instances for a heavy training job and shut them down after, paying only for what they used. This on-demand model influences system design by shifting the focus to software orchestration (like scheduling jobs on cloud GPUs) rather than physical installation. The availability of TPUs exclusively on Google Cloud also means if you design your system to use TPUs, you’re likely integrating with Google’s cloud ecosystem. In any case, whether on-premise or in the cloud, the hardware infrastructure for AI is built around these accelerators to handle the exponential growth of data and model sizes in modern AI.

• Architectural Integration: In designing an AI system, engineers must decide how CPUs (general processors) and GPUs/TPUs (accelerators) work together. Typically, the CPU acts as the orchestrator – loading data, running the overall program logic – while offloading the heavy mathematical crunching to the GPU or TPU. This means system architects plan for high-bandwidth connections between CPU and GPU/TPU, efficient data pipelines (so the accelerators aren’t waiting on data), and software frameworks that distribute tasks effectively. The presence of GPUs/TPUs in a system often dictates using certain libraries or programming models (like CUDA for NVIDIA GPUs, or XLA for TPUs). System design interviews for large-scale AI often touch on how you would incorporate accelerators into the architecture – e.g. using a cluster of GPU-equipped servers behind a service. The key is that GPUs and TPUs enable AI systems to be feasible at scale, so any modern AI infrastructure is built with them in mind, from hardware rack layouts to network topology.

• Continued Innovation: Finally, GPUs and TPUs drive continuous innovation in hardware infrastructure. For example, new networking architectures are developed to remove bottlenecks when hundreds of GPUs communicate during distributed training. Companies are also exploring novel chips (like NPUs – Neural Processing Units – and other AI accelerators) inspired by the success of GPUs/TPUs. The landscape of AI hardware is expanding, but GPUs and TPUs currently lead the way in influencing how we build the systems that power everything from deep learning workloads in the cloud to AI on the edge.

Modern AI hardware infrastructure has essentially been “co-designed” with these processors: as AI models grew, GPUs and TPUs were developed to handle them; in turn, having GPUs/TPUs allowed architects to create more ambitious AI systems. The result is a virtuous cycle of advancing hardware and more powerful AI applications.

Conclusion

In summary, GPUs and TPUs have revolutionized AI hardware infrastructure and system architecture. GPUs brought massive parallel computing power to AI, enabling the deep learning boom, while TPUs introduced an even more specialized boost for machine learning tasks. They influence how we design systems – from individual servers to entire data centers – by becoming the central pillars that handle the heavy lifting for AI workloads. For beginners, the key takeaway is that modern AI isn’t run on just generic CPUs; it’s powered by these specialized chips that make things like speech recognition, image search, and self-driving cars possible at scale.

Aspiring engineers and system designers should familiarize themselves with GPUs and TPUs, as this knowledge is increasingly important in the tech industry. Understanding these concepts will not only help you build better AI solutions but also prepare you for advanced discussions in your career (even in interviews!). If you’re excited to learn more about AI system design and hardware fundamentals, consider signing up at DesignGurus.io – explore courses like Grokking Modern AI Fundamentals to continue your learning journey. Happy learning!

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