Inside NVIDIA GPUs: How Billions of Transistors Drive the Future of AI and Graphics

Did you know a single high-end graphics chip now contains more transistors than there are stars in the Milky Way? The latest NVIDIA GPUs, like those based on the Blackwell architecture, have become the engines behind everything from cinematic gaming to AI supercomputers. But what exactly goes on inside these silicon marvels, and how do their inner workings enable such staggering performance?

The Anatomy of a Modern Nvidia GPU: Inside the Silicon Engine

At the core of every NVIDIA GPU lies a dense silicon die, measuring just a few square centimeters but packing in an astonishing number of transistors. For example, the most recent Blackwell flagship boasts 208 billion transistors, manufactured using TSMC’s advanced 4NP process. This transistor density is what allows the chip to execute trillions of operations per second.

The GPU is structured into a hierarchy of compute blocks:

• Graphics Processing Clusters (GPCs): The dominant building blocks, each GPC houses multiple specialized engines for rendering and computation.
• Streaming Multiprocessors (SMs): Each GPC contains multiple SMs. In the Blackwell GB202, there are 192 SMs, each with 128 CUDA Cores, 4 Tensor Cores, and a Ray-Tracing Core.
• CUDA Cores: These are the workhorses of the GPU, responsible for basic arithmetic operations. The RTX 5090, for example, features 24,576 CUDA Cores.
• Tensor Cores: Specialized for AI and matrix math, enabling fast neural network inference and training.
• Ray-Tracing (RT) Cores: Dedicated to accelerating ray-tracing calculations for realistic lighting and shadows in 3D graphics.

Each of these components works in harmony, allowing the GPU to process massive amounts of data in parallel—ideal for both real-time graphics and AI workloads.

Memory Hierarchy inside GPUS: Feeding the Compute Monster

To keep thousands of cores fed with data, GPUs employ a layered memory system:

• On-Chip Cache: The Blackwell GPU features up to 128 MB of fast L2 cache, while each SM has its own configurable L1/shared memory. This minimizes the need to fetch data from slower, off-chip memory.
• VRAM (Device Memory): High-bandwidth GDDR6X or HBM memory provides the main data storage for textures, AI models, and more. For instance, the memory controller on the GA102 chip supports a 384-bit bus with bandwidths exceeding 1 TB/s.
• Cache Hierarchy: Multiple levels of cache ensure that frequently-used data is quickly accessible, reducing latency and maximizing throughput.

This intricate memory hierarchy is crucial for real-time rendering and AI, where delays of even a few nanoseconds can impact performance.

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