- Artificial intelligence (AI) chips are specially designed computer microchips used in the development of AI systems.
- Nvidia produces a chip called the H100 accelerator, capable of processing such data and currently in high demand.
The AI industry is advancing at a rapid pace, with breakthroughs in ML and generative AI in the news almost every day. As AI technology develops, AI chips have become essential in creating AI solutions at scale. For example, delivering a modern AI application like facial recognition or large-scale data analysis using a traditional CPU—or even an AI chip from a few years ago—would cost exponentially more. Modern AI chips are superior to their predecessors in 4 critical ways: they’re faster, higher performing, more flexible and more efficient.
What Is AI Technology?
Artificial intelligence (AI) is transforming our world, and an important part of the revolution is the need for massive amounts of computing power. Machine learning algorithms are getting more complex every day, and require more and more computing power for training and inference.
At first, AI workloads ran on traditional central processing units (CPUs), leveraging the power of multi-core CPUs and parallel computing. Several years ago, the AI industry realised that graphical processing units (GPUs) were very efficient at running certain types of AI workloads. However, standard GPUs are no longer sufficient for those at the forefront of AI development, prompting the creation of increasingly specialised hardware.
However, standard GPUs are no longer sufficient for those at the forefront of AI development, prompting the creation of increasingly specialised hardware.
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The Leading AI Chip Manufacturers-NVIDIA
NVIDIA is currently the leading provider of AI chips. Previously known for its GPUs, in recent years NVIDIA developed dedicated AI chips, like Tensor Core GPUs and the NVIDIA A100, considered the most powerful AI chip in the world at the time of this writing.
The A100 features Tensor Cores optimized for deep learning matrix arithmetic and has a large, high-bandwidth memory. Its Multi-Instance GPU (MIG) technology allows multiple networks or jobs to run simultaneously on a single GPU, enhancing efficiency and utilisation. Additionally, NVIDIA’s AI chips are compatible with a broad range of AI frameworks and support CUDA, a parallel computing platform and API model, which makes them versatile for various AI and machine learning applications.
What role is Nvidia playing in the artificial intelligence industry?
Artificial intelligence programs, such as OpenAI’s ChatGPT-4, released last year to generate text, rely on massive databases and processing power. ChatGPT-4 is estimated to have 1.7 trillion parameters, ten times more than its 2020 predecessor, highlighting the growing complexity. Nvidia makes a chip called the H100 accelerator that is able to process that data and is in high demand. This month it announced it is making an even more advanced chip. Nvidia produces a chip called the H100 accelerator, capable of processing such data and currently in high demand.
