The modern era of artificial intelligence, characterized by deep learning and large language models, stands firmly on the shoulders of highly specialized hardware. Far beyond the capabilities of general-purpose CPUs, these dedicated “AI chips” or “AI accelerators” are the indispensable backbone, providing the computational horsepower required for both the arduous training of complex models and the rapid inference needed for real-time applications. Understanding this sophisticated hardware landscape is crucial for anyone seeking to grasp the true potential and limitations of contemporary AI.
The fundamental shift from traditional computing to AI workloads necessitated this hardware evolution. Conventional Central Processing Units (CPUs), designed for sequential processing and diverse tasks, excel at complex logic and control flow. However, AI, particularly deep learning, heavily relies on massive parallel computations—specifically, matrix multiplications and convolutions. These operations, performed billions or trillions of times, overwhelm CPUs due which lack the vast number of simple arithmetic logic units (ALUs) to execute them concurrently. This computational bottleneck spurred the development and adoption of Graphics Processing Units (GPUs) and subsequently, entirely new architectures optimized for AI.
Graphics Processing Units (GPUs): The AI Workhorse
GPUs are arguably the most ubiquitous and impactful AI hardware to date. Initially designed to render graphics by performing thousands of parallel operations on pixels and vertices, their architecture proved serendipitously perfect for the parallel nature of neural network computations. NVIDIA, a pioneer in this space, recognized this potential and developed CUDA (Compute Unified Device Architecture), a parallel computing platform and programming model that allowed developers to harness the GPU’s power for general-purpose computing (GPGPU).
Modern AI GPUs, such as NVIDIA’s A100 and H100 Tensor Core GPUs, feature thousands of processing cores, optimized for floating-point and integer operations crucial for deep learning. A key innovation has been the introduction of “Tensor Cores,” specialized units within the GPU that accelerate mixed-precision matrix operations, performing highly efficient fused multiply-add operations. This allows for significantly faster training and inference, especially when using lower precision formats like FP16 (half-precision) or bfloat16, which are often sufficient for deep learning tasks and conserve memory and bandwidth. The sheer parallelism and high memory bandwidth of GPUs make them indispensable for training large, complex neural networks, supporting models with billions or even trillions of parameters across multiple interconnected GPUs.
Tensor Processing Units (TPUs): Google’s Custom Solution
Google took a different approach by designing Application-Specific Integrated Circuits (ASICs) specifically for their TensorFlow and JAX