Market demand for AI computing and competing competitors

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In order to seize the opportunity of the future AI market, many companies are quickly promoting their AI-related projects, so there is an urgent need for chips that can perform high-performance computing. The following are some major examples:

Large technology companies : Large technology companies like Microsoft and Amazon are particularly fond of H100 chips. They are building AI-centric data centers, demonstrating the growing demand for high-performance AI hardware among these companies.
Cloud computing giants : Cloud computing giants like Meta, OpenAI and Stability AI are also using H100 chips to drive the next wave of AI development. These companies are using H100 GPUs to meet the surge in demand for generative AI training.
Server manufacturers : Some of the leading server manufacturers, such as Atos, Cisco, Dell Technologies, GIGABYTE, Hewlett Packard Enterprise, Lenovo, and Supermicro, offer servers and systems powered by NVIDIA H100 GPUs. The servers and systems of these companies support a wide range of AI applications, allowing the H100 chip to be widely used in different fields.
Companies that train LLM (Large Language Model) by themselves: including startups like Anthropic, Inflection, and Midjourney, as well as technology giants like Apple, Tesla, and Meta. They usually use the argentina whatsapp phone number computing power of external cloud service providers while purchasing their own GPUs to build their own stoves.
While NVIDIA's H100 chip is one of the most advanced GPUs on the market, especially in the fields of AI and high-performance computing, there are several other companies with competing products:

AMD ’s Radeon Instinct Series : AMD’s Radeon Instinct GPUs are designed for high-performance computing and deep learning applications.
Graphcore 's IPU ( Intelligence Processing Unit ) : Graphcore is a British company specializing in AI computing. Its IPU products are designed to specifically handle machine learning and AI work. IPUs offer different performance and efficiency advantages than NVIDIA's GPUs in certain specific AI applications.
Google ’s TPU ( Tensor Processing Unit ) : For applications specific to machine learning and artificial intelligence, Google’s TPU is an important option. These processors are designed for running machine learning frameworks like TensorFlow and provide optimized performance on certain AI jobs.