The difference between AI chips and GPU chips

On November 15, 2023, Microsoft launched two customized chips at the Ignite Developer Conference in Seattle. The two latest chips launched by Microsoft are the Maia chip, which is used to accelerate AI computing tasks, and the Cobalt chip, which uses the ArmCPU architecture. Maia chips are designed to run large language models, and both chips will be available in 2024. Both chips are manufactured using TSMC’s 5nm manufacturing technology. In addition, the way Maia chips are built is different from the network connection technology used by Nvidia. Maia chips are connected with standard Ethernet cables.

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On November 13, 2023, Nvidia launched its latest high-end chip for training artificial intelligence models, the H200, a graphics processing unit that powers the generative artificial intelligence boom. The H200 includes 141GB of next-generation "HBM3" memory, which will help it generate text, images or predictions using AI models. The popularity of Nvidia's AI GPUs has fueled the company's growth. The market expects the company's sales to surge 170% this quarter.

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AI chips and GPU chips are both processor chips, but their designs and uses are slightly different. So what is the difference between AI chips and GPU chips?

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1. Definition of AI chip

An AI chip is a special processor designed to support the computing needs of artificial intelligence applications. AI chips usually use highly optimized algorithms and can quickly handle a large number of matrix calculations, vector calculations, neural network calculations and other tasks. AI chips are usually used in computer vision, speech recognition, natural language processing, machine learning and other fields.

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Common AI chip products include:

NVIDIA’s Tesla V100, A100, T4, etc.;

Google's TPU (Tensor Processing Unit);

AI chip series products from NVIDIA, Intel, Broadcom and other manufacturers.

2. Definition of GPU chip

A GPU chip is a graphics processor that is mainly used in graphics applications such as graphics rendering and processing. Compared with CPU chips, GPU chips have faster speeds and higher parallel processing capabilities in graphics rendering, so they are also widely used in scientific computing, cryptography, machine learning and other fields.

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Common GPU chip products include:

NVIDIA's GeForce, Quadro, Tesla, etc.;

AMD's Radeon, FirePro, etc.;

Intel's Xe etc.

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3. What is the difference between AI chip and GPU chip?

Although both AI chips and GPU chips can be used for artificial intelligence tasks such as machine learning, their designs and uses are somewhat different:

The design of AI chips is more focused on supporting neural network calculations, while GPU chips are more focused on graphics rendering;

AI chips usually use more optimized computing algorithms and more efficient hardware architecture to complete specific tasks faster;

AI chips are more suitable for tasks such as large-scale matrix calculations and neural network calculations, while GPU chips are more suitable for tasks such as general numerical calculations and graphics rendering.

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