NVIDIA
GPU and AI accelerator chip designer
NVIDIA is an American semiconductor company that designs graphics processing units, or GPUs, originally built for rendering computer graphics but now widely used to accelerate artificial intelligence training and inference, scientific…
Definition
NVIDIA is an American semiconductor company that designs graphics processing units, or GPUs, originally built for rendering computer graphics but now widely used to accelerate artificial intelligence training and inference, scientific computing, and data center workloads. The company does not manufacture its own chips; it designs them and has them fabricated by external foundries, then sells them as consumer graphics cards, data center accelerators, and embedded computing modules.
Overview
NVIDIA started as a graphics card maker focused on making 3D rendering fast enough for video games, an application that demanded performing the same simple mathematical operation on millions of pixels simultaneously. That requirement produced a chip architecture built around thousands of small, parallel processing cores rather than the handful of large, general-purpose cores found in a CPU, and it is this same parallel architecture, later exposed to general-purpose programmers, that made GPUs unexpectedly well suited to training neural networks. Mechanically, a GPU splits a large computation, such as multiplying the huge matrices used in a neural network layer, into thousands of smaller pieces that run at the same time across its cores, whereas a CPU executes far fewer instruction streams but handles branching logic more efficiently. NVIDIA's CUDA programming platform lets developers write code that runs directly on this parallel hardware, and it became the de facto standard toolkit for GPU-accelerated computing, which is one reason NVIDIA's chips became closely tied to the growth of deep learning research and deployment. Within the chip industry, NVIDIA differs from CPU makers like Intel and AMD in that it specializes almost entirely in parallel accelerators rather than general-purpose processors, and it differs from Arm or RISC-V, which are instruction set architectures rather than chip products themselves. NVIDIA's closest direct rivals in AI accelerators are AMD's GPU line and various custom AI chips built by cloud providers, while in gaming graphics its long-standing competitor is AMD's Radeon line. In practice, NVIDIA GPUs run inside gaming PCs, workstations for video and 3D content creation, and, most consequentially in recent years, data center servers used to train and run large AI models. Cloud providers rent access to NVIDIA GPU capacity, and AI research labs and companies buy or lease large clusters of these chips to train models that would be impractical to train on CPUs alone. The reliance on GPUs for AI comes with trade-offs: they draw substantial power and generate significant heat at data-center scale, and demand has at times outpaced supply, creating shortages and driving up prices for both consumers and enterprise buyers. NVIDIA's CUDA ecosystem also creates a degree of vendor lock-in, since software optimized for CUDA does not automatically run efficiently on competitors' hardware, which is part of why alternative software stacks and custom AI chips built by cloud providers have emerged as attempts to reduce dependence on a single vendor over the long run.
Key Concepts
- Designs GPUs optimized for massively parallel computation
- Maintains CUDA, a widely adopted parallel computing programming platform
- Produces data center accelerators used for training and running AI models
- Produces GeForce consumer graphics cards for gaming and creative work
- Produces Jetson modules for embedded and edge AI computing
- Builds full server systems, such as DGX, combining multiple GPUs
- Outsources chip fabrication to external semiconductor foundries
- Develops software libraries that accelerate common AI and scientific workloads