Welcome to AI Hardware & Chips, the engine room of modern artificial intelligence. This corner of Technology Streets explores the silicon, systems, and architectures that make today’s models possible—from training trillion-parameter networks to running real-time inference at the edge. Here, raw compute meets clever design as CPUs, GPUs, TPUs, NPUs, and custom accelerators battle limits of power, heat, and bandwidth. You’ll dive into how memory hierarchies shape performance, why interconnects matter as much as cores, and how chiplets and advanced packaging are redefining Moore’s Law. We cover data center monsters built for scale, compact edge chips tuned for efficiency, and the global supply chains that quietly influence AI’s future. Whether you’re curious about wafer fabs, instruction sets, or why certain workloads scream on one architecture and crawl on another, this section connects algorithms to atoms. Step inside the hardware that turns math into intelligence—and discover what really powers the AI revolution.
A: They excel at massively parallel math operations.
A: A chip optimized for specific AI workloads.
A: Training learns models; inference runs them.
A: AI workloads move huge amounts of data.
A: For scale and efficiency, often yes.
A: Power, heat, and memory bandwidth.
A: Not always—architecture matters more.
A: No, they complement each other.
A: Sustained AI loads generate extreme heat.
A: Co-designing hardware and models together.
