Why GPUs and tensor cores excel at matrix multiplication
The fundamental change in AI chip design revolves around their ability to perform highly parallel mathematical operations at an unparalleled scale. Traditionally, a classic CPU would execute one instruction per cycle, evolving to modern CPUs that handle a couple of 10 instructions across multiple cores.
In stark contrast, modern AI cards, often referred to as AI accelerators, can process more than a hundred thousand instructions per cycle. This dramatic difference in throughput is central to understanding “how AI chips work.”
At their core, AI accelerators are derivatives of Graphics Processing Units (GPUs), initially developed for rendering complex graphics in arcade gaming 50 years ago or personal computers, such as Nvidia’s GeForce 256 released in 1999.
The basic idea of a GPU is that it works with vectors, matrices, or more generally, tensors, rather than individual values. This architecture proved exceptionally well-suited for the matrix multiplication operations that are ubiquitous in neural network computations.
For example, Google’s specialized AI chips are named Tensor Processing Units (TPUs) to reflect this core function. The cores within these modern GPUs, often called tensor cores, are specifically engineered to efficiently multiply large matrices—rows and columns of numbers—in a single cycle.
This rapid matrix multiplication provides the immense speed necessary to train and run the large language and image models that define modern generative AI. While CPUs can perform tens or even thousands of floating-point operations per cycle, a GPU can now do over a hundred thousand, illustrating the vast difference in parallelization.
The field of AI chip manufacturers is dynamic, with several major players. Nvidia remains a dominant force, with its A100 being the current workhorse powering the AI revolution, and the new H100 representing the next generation.
Other significant contributors include Intel, which offers its Gaudi and Gaudi 2 accelerators, alongside its Arc graphics cards. AMD also competes in this space. Cloud providers are increasingly developing their own custom silicon; Google fields its TPUs, while Amazon offers Trainium for training AI models and Inferentia for inference tasks.
A key differentiator among these hardware providers is the maturity of their software ecosystems. As venture capital firm a16z special advisor Guido Apenzeller points out, while pure hardware statistics like floating-point operations per second can be competitive across different chips, Nvidia holds a significant advantage due to its mature software ecosystem.
Their CUDA platform makes it considerably easier for AI engineers to optimize models. For instance, an engineer can take an open-source model, and with Nvidia’s CUDA system, it often runs efficiently out of the box with all necessary optimizations. For other chips, developers might need to undertake substantial optimization work themselves, which can be a barrier to adoption.
Optimizations extend to how numbers are represented and processed. Typically, a floating-point number is represented in 32 bits, consisting of:
- A first bit for the sign
- The next eight for the exponent
- The subsequent 23 for the fraction
However, developers have found ways to reduce this precision, sometimes to 16 bits or even eight bits, by carefully normalizing calculations to avoid overrunning or underrunning values.
These “tricks” allow AI developers to squeeze more performance out of existing chips, illustrating the deep integration between hardware capabilities and software ingenuity. To learn more about specialized AI chips, consider reading How AI Uses Specialized Chips for Fast Computation.
Surging hardware demand and rising data center cooling challenges
The advent and rapid adoption of specialized AI chips have created profound ripple effects across the technology industry. One of the most immediate consequences is the intense demand for this specialized hardware, which currently “outstrip Supply by a factor of 10.”
This scarcity has led to significant challenges for even established AI companies, making access to high-performance AI accelerators a strategic imperative. The substantial capital investment required for these chips also shapes the field of AI development, favoring well-funded entities or those with cloud infrastructure partnerships.
The drive for parallel processing, while enabling unprecedented computational power, has also introduced new engineering challenges. Modern AI chips, such as the high-end graphics cards used for gaming or AI, can draw hundreds of watts of power. This escalating power consumption translates directly into increased heat generation.
Data centers housing these accelerators are increasingly experimenting with and deploying novel cooling solutions, including liquid cooling, to manage the extreme energy densities. This highlights a shift where advancements are not solely about shrinking transistors but also about efficient power delivery and thermal management.
The industry is effectively adapting to a new era where raw computational capability must be balanced with practical operational concerns. Discover more about the challenges of AI chip supply in AI Chips Supply Chain: Hidden Circuit Board Risks.
Growing competition to rival Nvidia and expand chip supply
Looking ahead, the evolution of AI chips will continue along several key trajectories. While Moore’s Law for transistor density remains active, the focus for performance gains is increasingly on architectural innovations and software-hardware co-design, rather than solely on individual core speed. The need for greater parallelization will intensify, pushing the boundaries of tensor operations and specialized core designs.
The competitive field among chip manufacturers is set to intensify. While Nvidia currently enjoys a strong position due to its mature CUDA software ecosystem, competitors like Intel, AMD, and major cloud providers (Google, Amazon) are investing heavily in their own hardware and software solutions.
The success of these alternative platforms will hinge on their ability to build solid, developer-friendly ecosystems that can rival Nvidia’s established advantage. Questions around which chips are best suited for particular tasks, such as training versus inference, will also become more pronounced. For more on this, see How Are AI Chips Different From Just GPUs?.
The ongoing challenge of meeting the overwhelming demand for AI hardware will necessitate creative solutions, from optimizing existing inventories to exploring new manufacturing capacities.
The interplay between compute power, capital investment, and technological advancement will remain a central theme, shaping access to and the future direction of artificial intelligence. The ability to innovate across hardware, software, and cooling solutions will define the next generation of AI chips and the capabilities they open up.
