GPUs vs. CPUs: Demystifying Hardware Processors

While GPUs get most of the attention these days, CPUs have been and will continue to be a mainstay among enterprise IT hardware. But they were not designed to support the heavy processing requirements of AI workloads that have evolved over the past few years. As a result, many new types of processing units, such as GPUs, have emerged, with more on the way. AI providers also continue to develop new technologies that enhance how existing processors function.

In my most recent blog post, I wrote that selecting the right AI hardware starts with asking the right questions; this applies across the board. To ensure the best purchase decisions, you must identify your organization’s workload processing needs before you choose your equipment. For instance, training AI models requires significantly more compute than running industry-specific inference models at the edge. GPUs will likely be your choice for any large training workload. But a CPU can perform other tasks far more efficiently than a GPU, so understanding the workload is critical.

Whether to buy or lease hardware and where to run your equipment are also part of the decision-making process. Ensuring you can access adequate power, cooling and networking is integral to ensuring that you get the most from your hardware purchases.

While the focus here is on GPUs and CPUs, I’ll also introduce other processing hardware including LPUs, NPUs and TPUs, all specialized processors designed for specific AI tasks.

Choosing GPUs, CPUs or both

It’s important to note that both GPUs and CPUs play a role in modern computing; it’s not an either-or scenario, as the blog title might have implied. They enable specific types of workload processing: serial processing for CPUs and parallel processing for GPUs.

I often use bricklaying as an example when I talk about how CPUs and GPUs operate. With CPUs, think of it like a master mason laying one brick at a time. With GPUs, it’s like hiring 1,000 bricklayers at once. They can build a wall in seconds when they all coordinate and stay in sync; everyone knows exactly when and where to lay their bricks.

GPUs (graphics processing units) have exceptional power and excel at processing thousands of threads simultaneously. Originally designed for rendering graphics in video games, GPUs now power the massive computations associated with large language models (LLMs), as well as deep learning and neural network training. GPU processing power is known for driving breakthroughs in fields like drug discovery and algorithmic high-frequency trading that wouldn’t be possible with CPUs.

GPU hardware and power costs are significant factors, as some AI workloads require very high levels of GPU-powered compute. Using GPU-powered hardware inside private data centers can quickly become cost-prohibitive. Further, while GPUs are efficient when running calculations, they may end up sitting idle if not deployed properly, resulting in a wasted investment.

CPUs (central processing units) perform well in environments that require versatility, precise control and rapid responsiveness, due to their strengths in sequential task execution and processing requests as quickly as possible, which minimizes latency. A host of technologies run on sequential task program logic—meaning a request must generate a result before the next step in the program begins. Think of operating systems, word processing and browsing, and a range of devices and systems, including PCs, servers, mobile and embedded systems. On the AI front, CPUs are used for inference and may be suitable for training or fine-tuning smaller, domain-specific AI models that don’t require the same level of parallel processing as LLM training.

There are also cases where it’s beneficial to use CPUs and GPUs together in single workstations or server setups. For example, 3D modeling, animation and large-scale data analysis often rely on high-end CPUs paired with multiple GPUs to distribute complex workloads efficiently.

Defining specialized processors: beyond GPUs and CPUs

While GPUs are excellent at performing many processes at the same time, they aren’t as versatile as CPUs. Other types of processors specialize in making various aspects of AI workload processing more efficient:

• LPUs (language processing units), developed by Groq, specialize in natural language processing (NLP) tasks and excel at processing large language models, text comprehension and speech-to-text conversions. Optimized for linguistic computations, LPUs efficiently power applications like chatbots, voice assistants, real-time translation and sentiment analysis.
• NPUs (neural processing units) accelerate AI and ML tasks in neural network training and inference. They are energy-efficient and can handle complex computations, such as matrix multiplication, which makes them a good fit for AI workloads in smartphones, IoT devices and robotics.
• TPUs (tensor processing units), developed by Google, accelerate AI workloads and are highly efficient at deep learning tasks like training and inference. TPUs are optimized for tensor computations, enabling high performance in neural network processing and making them a good fit for large-scale AI models and applications.

Each type of processor, including GPUs, uses a specific instruction set architecture (ISA) that determines how it will operate and respond to specific instructions. At the basic level, it’s a matter of doing math inside a computer, whether it’s matrix math or vector math.

A lot is going on among innovators in the processing space. They’re constantly striving to achieve better power efficiency while increasing the number of instructions per clock cycle. There are other ISA options for CPU aside from the tried-and-true x86.  For example, RISC-V is an open-source ISA that will allow hardware providers to build lower-cost processors without burdensome licensing arrangements. These innovations are changing how we will process data in the future, making solutions smarter and more scalable. Other new developments to watch are the new photonic interfaces that use light for the bus between the processor and other components, instead of electrons on a copper trace.

Groq, an Equinix partner, is using a specific type of core and a specific type of math on their LPUs, along with field programmable gate arrays, to support faster and more resource-efficient inference. This meets the demand for processing hardware that uses less power and is more efficient with massive amounts of data.

Deciding whether to buy or lease

It’s essential to derisk your choice. If you purchase GPUs for training but end up needing to shift your focus to inference, you may end up with expensive, unused capacity. But for large pharmaceutical and financial services companies, buying the hardware is the only choice. They need to protect their regulated, proprietary data.

For other organizations, renting capacity could be a helpful alternative. Like multicloud computing, leasing the hardware you need from different providers lets you choose the combination that best fits your requirements–without making CAPEX investments or getting locked into a single vendor. Pricing is token-based, which equates to how much data you need to process.

Leasing removes barriers to entry; you simply learn how to use the hardware and feed it data. This option may be more palatable for smaller companies with limited data. Small businesses without extensive proprietary data may opt to buy or build the software they need to operate their company while renting the compute power needed to run the software.

Something else to keep in mind is the fact that, due to the accelerated pace of innovation among AI chip manufacturers like NVIDIA, also an Equinix partner, GPUs “age like milk,” lasting perhaps 18 months instead of the 60-month cycle typical for previous generations of hardware. This may make leasing a logical choice for many businesses.

Deploying compute hardware in the right places

Whether you’ve purchased or leased your hardware, you need to park it in an environment with adequate power, cooling and networking. Until you plug your hardware into a network, it’s just an expensive heater. That’s where Equinix Distributed AI™ infrastructure comes into play. It enables businesses to move datasets quickly, securely and intelligently across clouds, centralized infrastructure and edge processing locations.

Our ecosystem of more than 10,000 enterprises and service providers—from established cloud providers to emerging AI specialists—helps you find the partners you need to get started right with distributed AI. At Equinix, you can purchase hardware or lease it on a pay-as-you-go basis from ecosystem GPU providers and GPUaaS partners. Then you can deploy it in any of our 270+ AI-ready data centers in 77 markets worldwide, with the liquid cooling and interconnection solutions you need to run your hardware and connect data, compute, models and inference engines across regions and clouds.

Learn more about how you can make the most of your AI hardware: Read the white paper, The engine of AI powering innovation at scale.