Artificial intelligence (AI) has rapidly evolved over the last few years and is transforming the way we live, work, and communicate. AI relies on powerful computer hardware that can handle the computational demands of machine learning algorithms. NVIDIA is a leader in providing hardware solutions for AI applications. In this blog post, we’ll explore the different types of computer chips used for AI and specifically look at NVIDIA’s AI hardware offerings.

What are the Different Types of Computer Chips Used for AI?

There are three primary types of computer chips used for AI applications:

  1. CPUs (Central Processing Units): CPUs are the standard computer chips that perform basic operations and instructions. They are not specialized for AI and can be used for other general computing tasks. However, CPUs are not efficient enough for running large-scale AI algorithms.
  2. GPUs (Graphics Processing Units): GPUs are specialized computer chips designed for parallel computing. They can handle thousands of calculations simultaneously, making them ideal for AI applications that require massive amounts of data processing.
  3. TPUs (Tensor Processing Units): TPUs are specialized chips developed by Google for their TensorFlow AI framework. They are specifically designed to accelerate machine learning algorithms.

GPUs are currently the most commonly used computer chip for AI applications.

They offer a powerful and efficient way to train machine learning models by parallelizing the computations. NVIDIA’s Tesla GPUs are widely used for AI tasks and offer high performance and energy efficiency.

NVIDIA’s AI Hardware Offerings

NVIDIA offers a range of AI hardware solutions that are optimized for different AI applications. Here are some of NVIDIA’s AI hardware offerings:

  • Tesla GPUs: NVIDIA’s Tesla GPUs are NVIDIA’s flagship product line of AI hardware. They are specifically designed for AI workloads and offer high performance and energy efficiency. Tesla GPUs are used for deep learning, data analytics, scientific computing, and other AI applications that require massive amounts of data processing. Tesla GPUs are evolving with each generation, offering higher performance and more advanced features. NVIDIA’s latest Tesla GPUs, such as the A100 and A40, are designed to meet the growing demand for AI applications and offer unprecedented acceleration for training and inference tasks.
  • Jetson TX2: NVIDIA’s Jetson TX2 is a compact, energy-efficient AI computer that can be integrated into edge devices such as robots, drones, and autonomous vehicles. It is designed for AI applications that require real-time data processing and can operate under strict power and size constraints. Jetson TX2 is evolving to become more powerful and energy-efficient, with each new generation offering improved performance and new features. NVIDIA’s latest Jetson TX2 model, the NX, is designed to handle more complex AI applications and support additional sensors and cameras.
  • DGX Systems: NVIDIA’s DGX systems are integrated AI supercomputers that are optimized for deep learning training and inference tasks. They offer unmatched performance and scalability, making them ideal for organizations that require large-scale AI solutions. DGX systems are evolving to become more powerful and flexible, with each new generation offering more advanced features and capabilities. NVIDIA’s latest DGX model, the A100, is designed to offer unprecedented acceleration for AI applications and includes advanced networking features and support for the latest AI frameworks.

Computer chips play a crucial role in the development and deployment of AI applications. NVIDIA’s AI hardware offerings, such as Tesla GPUs, Jetson TX2, and DGX systems, provide powerful and efficient solutions for different AI workloads. With the increasing demand for AI applications, it is essential to choose the right hardware solution that can handle the computational demands of these tasks.