From Transistors to AI: How Modern CPUs and GPUs Power Computing

This explainer walks through how a CPU uses billions of microscopic transistors to perform computations, starting from NMOS and PMOS switches to the building blocks of logic gates, adders, and the arithmetic logic unit. It then follows how data moves through registers and caches to RAM and storage, and how GPUs enable massive parallel processing for AI tasks. The video ties these ideas together to show how modern CPUs and GPUs shape computing today and why AI workloads drive demand for powerful semiconductors.

• Transistors as switches and the NMOS/PMOS distinction
• Logic gates to half adders and full adders enabling binary addition
• Memory hierarchy from registers and caches to DRAM and SSD
• GPU parallelism powering AI workloads and large-scale data centers

Introduction: The CPU as the heart of the computer

The video opens with a description of the central processing unit as the brain of a computer, sitting on a silicon chip packed with billions of transistors. The core question is what those tiny switches actually do. Transistors act as switches controlled by electricity, and by shrinking them we can pack more of them into the same area. That density reduces signal travel time and increases computing power, a key driver behind the AI data center boom.

Transistors: NMOS and PMOS in a binary world

The transistors discussed are MOSFETs, coming in two flavors: NMOS and PMOS. An NMOS normally blocks current, but applying a gate voltage turns it on and allows current to flow, while removing the voltage turns it off. A PMOS does the opposite. Computers ultimately rely on binary digits, ones and zeros, which are produced by transistors switching on and off. This binary foundation underpins every image, sound, keystroke, and calculation processed by a modern CPU.

From switches to gates: building logic and arithmetic

To compute, transistors are arranged into logic gates such as not gates, NANDs, NORs, and ORs. These gates are represented by symbols and can be combined to create more complex functions. When you want to add two decimal numbers, the same principle applies in binary: you combine bits with logic gates to generate a sum and a carry. The video shows a half adder with two inputs and two outputs for the sum and carry, and then a full adder built from two half adders and an OR gate. A handful of adders chained together process multi-digit binary numbers. A historically simple design for adding two numbers used around two hundred transistors, illustrating how the core arithmetic logic grows with bigger numbers and more operations like subtraction, multiplication, and comparison. This work is carried out by the arithmetic logic unit, or ALU, inside each CPU core.

Inside a CPU core: registers, caches, and the ALU

Inside a CPU core, the ALU receives operation instructions and operands from registers, computes the result, and writes it back to a register. The data then moves through the memory hierarchy: from DRAM to caches and into registers, with the program counter steering the sequence of instructions. Processors fetch instructions from an L1 instruction cache, possibly hitting L2 or L3 caches or requesting data from DRAM. A memory controller retrieves blocks of nearby memory to speed up subsequent instructions. The L1 data cache holds values being worked on, while L2 and L3 caches expand the working set. This local fast memory is essential because registers and caches are tiny yet extremely fast, while SSDs provide permanent storage for the operating system and files, loaded into RAM as needed.

From single core to multi-core and memory hierarchy

Modern CPUs contain multiple cores that can execute several streams of instructions in parallel. While each core has its own fast local memory, they share access to the L3 cache. The story also underscores how data flows from external storage through memory into the CPU and back, a cycle that is sped up by caches and intelligent prefetching. The video also notes the GPU’s role in rendering and heavy numerical workloads, showing how billions of transistors and thousands of smaller processing units enable parallel processing across large blocks of data, a workload shape well suited to AI tasks that require enormous multiply-add operations across matrices and tensors.

GPUs and AI workloads: parallel processing at scale

GPUs are designed to handle large-scale parallelism, distributing the same basic calculations across many processing units. AI models repeatedly perform numerous multiplications and additions on large data sets, and GPUs can dramatically accelerate these tasks by processing many independent elements at once. This is why modern AI training and inference rely heavily on GPUs, and why the AI data center boom is increasing demand for GPUs, memory, and other semiconductor components.

Putting it all together: the motherboard and the broader system

The video wraps up by describing how everything is connected on a motherboard, a printed circuit board that links the CPU, RAM, storage, GPU, and other components with traces that carry power and data signals. While sponsorship details appear in the video, the core message remains: the journey from billions of switches on a silicon wafer to the powerful computing systems that drive AI is a story of layered technologies, architectural choices, and the relentless pursuit of efficiency and speed.

Conclusion: a snapshot of current computing power and AI demand

The takeaway is that the combination of transistor scaling, efficient logic design, memory hierarchies, parallel processing, and a robust platform for connecting components drives today’s computing capabilities. As AI workloads grow, the semiconductor industry faces both heightened demand and the challenge of supply and price stability for GPUs, memory, and other hardware essential to AI acceleration and general-purpose computing.