What is Hardware Acceleration (GPUs/TPUs) for AI?
Introduction to Hardware Acceleration
Hardware acceleration for AI means running the math of neural networks on processors built for massively parallel arithmetic instead of on general-purpose CPUs. Many of the core ideas in deep learning, such as backpropagation, were worked out decades ago. Training large networks became practical once researchers could run that math on hardware with far more parallel throughput than a CPU.
A CPU (Central Processing Unit) has a relatively small number of powerful cores. Server CPUs today range from a handful of cores to more than a hundred. Each core is built for fast, branching, general-purpose work, such as running an operating system or a database. CPUs also have vector units and can run neural networks, but their total arithmetic throughput is limited compared with accelerators.
Training and running a neural network is dominated by a small set of operations, mostly matrix multiplications and related tensor operations. Those operations are highly parallel: the same multiply-and-add is applied across large arrays of numbers. Hardware that can perform many of those operations at once speeds up both training and inference.
GPUs: The Workhorse of AI
The GPU (Graphics Processing Unit) was designed to render graphics, which also requires applying the same calculations to large numbers of pixels and vertices in parallel. GPUs have thousands of simpler cores that run many threads at once. NVIDIA’s CUDA platform, released in 2007, made those cores programmable for general computation, and researchers soon showed that GPUs could train neural networks much faster than CPUs.
Today GPUs from NVIDIA, AMD, and others are the most widely used accelerators for training and serving AI models. Frameworks such as PyTorch, TensorFlow, and JAX run on them through vendor libraries like cuDNN and ROCm.
Specialized Units: Tensor Cores and TPUs
As models grew, chip designers added hardware dedicated to the matrix math that dominates deep learning.
NVIDIA Tensor Cores
Starting with the Volta generation, NVIDIA GPUs include Tensor Cores, units that perform small matrix multiply-and-accumulate operations in hardware, typically at reduced precisions such as FP16, BF16, FP8, or INT8. Deep learning frameworks route eligible operations to Tensor Cores, which gives large speedups over the GPU’s general-purpose cores for those workloads.
Google TPUs (Tensor Processing Units)
Google designed its own accelerator, the TPU. Google describes TPUs as custom application-specific integrated circuits (ASICs) built to accelerate machine learning workloads. TPUs are built around large matrix multiply units and are available in Google Cloud and used inside Google’s own services.
TPUs are not tied to TensorFlow. They are programmed through the XLA compiler, and Google Cloud supports running JAX, PyTorch (through PyTorch/XLA), and TensorFlow on Cloud TPU. Many recent large-model projects use JAX on TPUs.
Other Accelerators
The market also includes AMD Instinct GPUs, AWS Trainium and Inferentia chips, and neural processing units (NPUs) built into phones and laptops, such as Apple’s Neural Engine.
Training vs. Inference
Hardware requirements differ by phase of the AI lifecycle.
- Training: Training a large foundation model uses many accelerators connected by high-bandwidth links (for example NVIDIA NVLink within a server and InfiniBand or Ethernet between servers), running for weeks or months. Memory capacity and interconnect bandwidth matter as much as raw compute.
- Inference: Serving a trained model needs far less compute per request, but it runs continuously and at scale. Large hosted models still run on data center GPUs and TPUs. Smaller or quantized models (weights stored at lower precision, such as 8-bit or 4-bit) can run on consumer GPUs, laptops, and on-device NPUs.
Conclusion
Modern AI depends on hardware that can run tensor math in parallel at high throughput. GPUs remain the most common choice, while TPUs and other purpose-built accelerators target the same matrix-heavy workloads with different trade-offs in cost, power, and software support. For data teams, the practical questions are which frameworks the hardware supports, how much memory the models need, and whether a workload is training or inference.
Learn More
To learn more about the Data Lakehouse, read The Lakehouse Built for Everyone by Alex Merced. You can find this and other books by Alex Merced at books.alexmerced.com.