AI runs on specialized hardware because training and inference are mostly massive amounts of matrix multiplication that can be done in parallel. GPUs…
See why AI moved off ordinary CPUs onto parallel chips.
Under the hood, training and running a neural network is overwhelmingly matrix multiplication — huge numbers of small multiply-and-add operations that don't depend on each other. That independence means they can all happen at once, if you have hardware with enough parallel units.
A regular CPU has a handful of powerful cores optimized for doing varied tasks one after another. AI doesn't need that; it needs thousands of simple operations in parallel, which is why the field moved to hardware built for exactly that.
Compare the three chip types and where each fits.
A CPU is a general-purpose processor: flexible, great for varied sequential work, but with too few cores for large-scale AI math. A GPU has thousands of cores designed for parallel numeric work; originally for graphics, it turned out perfect for neural networks and now dominates AI.
A TPU is an ASIC — a chip custom-built by Google for machine learning — squeezing out efficiency for tensor operations, available mainly through Google Cloud. GPUs are the flexible default across the industry; TPUs are a strong option within Google's ecosystem.
Understand why VRAM and bandwidth matter more than raw speed for LLMs.
For large language models, the first question is whether the model even fits: the weights must sit in the chip's VRAM. A model with billions of parameters needs many gigabytes, which is why big models require large or multiple GPUs — and why quantization, which shrinks the weights, is so useful.
The second limit is memory bandwidth. Generating each token means reading the whole model's weights from memory, so inference speed is often capped by how fast memory can be read, not by how fast the cores compute. This is why LLM inference is called memory-bound.
Because memory rules, people pick hardware by VRAM size and bandwidth, use quantization to fit bigger models, and batch requests to reuse each weight read across many users — all consequences of the memory bottleneck.
Match hardware to the task and avoid beginner errors.
Most people don't buy AI chips; they rent them from cloud providers by the hour, scaling up for a training run or a serving spike and down afterward. For inference, choose a GPU whose VRAM fits your (possibly quantized) model with room for the working memory each request needs. Training large models needs far more — often many GPUs networked together.
For small models or experimentation, a single consumer GPU or even a modern laptop can be enough, especially with quantized models.
Watch for: choosing a chip on headline compute (TFLOPS) while ignoring VRAM and bandwidth, then finding the model doesn't fit or inference is slow; forgetting that serving needs memory beyond the weights for the attention cache; and buying hardware when renting would be cheaper and more flexible. Size by memory first.
AI uses specialized hardware because its math is massively parallel matrix multiplication. GPUs, with thousands of cores, dominate; TPUs are Google's custom ML chips; CPUs are too sequential for scale. For LLMs the real limit is memory: the weights must fit in VRAM, and reading them per token makes inference memory-bandwidth-bound — which is why quantization and batching matter. Size hardware by VRAM and bandwidth first, and rent from the cloud rather than buying when you can.
You want to serve a quantized open model for a small app. Explain how you would decide which GPU you need, why VRAM and bandwidth matter more than headline compute, and why renting by the hour beats buying a card outright.
Why does AI use specialized hardware like GPUs?
Neural network math is massively parallel, so hardware with thousands of parallel cores (GPUs) vastly outperforms a few sequential CPU cores.
What is a TPU?
TPUs are purpose-built ML accelerators; GPUs remain the flexible industry default while TPUs shine within Google's ecosystem.
Why is memory often the bottleneck for LLM inference?
LLM inference is memory-bound: fitting the weights and reading them per token limits it more than raw compute, which is why quantization and batching help.
What is a common hardware-selection mistake?
Because LLMs are memory-bound, sizing by VRAM and bandwidth first — not TFLOPS — is what matters, plus room for the attention cache.