Artificial intelligence (AI) might sound like a new concept, but it’s actually quite old. Scientists and mathematicians in the U.S. have been exploring various AI concepts and theories since the 1940s. However, it was in February 1996 that the relationship between AI and humans changed significantly. One of the first AI computers, Deep Blue, defeated a world champion in a chess match. This event marked a significant milestone in the development of AI.
The ultimate winner of the AI race might not be OpenAI, Google or Microsoft, but actually NVIDIA. In the last 18 months, NVIDIA’s market capitalisation has skyrocketed almost 10 times, from USD 280 billion to USD 3.012 trillion, making it the second most valuable company ahead of Google and Apple. The reason for its dominance is its GPUs, which power 88 per cent of the world’s AI infrastructure. Whether it’s ChatGPT or Gemini, all these AI models rely on NVIDIA GPUs for training and usage.
OpenAI uses 10,000 NVIDIA A100 GPUs to train GPT 4, and NVIDIA’s next-gen H100 GPUs are even more powerful. Meta plans to purchase 350,000 of these GPUs for an astonishing USD 9 billion, and Meta is just one company. Other big and small companies, including OpenAI, Google, Amazon, and even Apple, are in the race. What makes NVIDIA GPUs so special? They introduced CUDA (Compute Unified Device Architecture) in 2006. This is a computing platform and programming model that transforms NVIDIA GPUs from serial to parallel computing.
To explain the difference, simply think of serial computing as a single boat taking limited passengers across a river in one go, whereas parallel computing uses multiple smaller boats to carry passengers simultaneously. This reduces the workload and increases efficiency. This parallel processing, needed to create lifelike graphics, is also ideal for deep learning, which is the backbone of artificial intelligence development.
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