Introduction
The demand for more powerful and efficient hardware to train AI foundation models has led to ground-breaking innovations. One such innovation is the advent of GenAI chips. These cutting-edge processors are designed to push the boundaries of AI capabilities, unlocking new possibilities, and reshaping the future of computing.
What are GenAI Chips?
GenAI chips are not your average processors. They are specialised chips made targeting AI tasks. Regular CPUs and GPUs are like jacks of all trades, good for general stuff, but GenAI chips are the specialists. They are far more efficient at dealing with the complicated math involved in machine learning and deep learning. This specialised focus makes them blazing fast and efficient in handling AI tasks. GenAI chips accelerate neural network computations. Neural networks, the building blocks of AI, require vast amounts of computational power for training and inference. GenAI chips optimize these operations, significantly reducing processing time and energy consumption compared to conventional hardware.
What difference will they make?
GenAI chips are poised to play a pivotal role in bringing AI capabilities to devices at the edge enabling smart devices, ranging from smartphones to IoT devices, to perform complex AI tasks locally. GenAI chips could advance to a stage where they are deployed across a spectrum of applications, ranging from autonomous vehicles and medical diagnostics to industrial automation and beyond. Delivering the ability for real-time processing and rapid inference makes GenAI chips invaluable in scenarios where quick machine-driven decision-making is crucial.
Do GenAI chips exist today?
A strand of human hair has a width of about 100,000 nanometers. Half a century ago the smallest semiconductor transistors were about 12,000 nanometers in width. Nvidia’s A100 GPU, a widely used AI chip in the world today has transistors that are 7 nanometers wide. Google’s latest tensor processing unit (TPU) a credible alternative to Nvidia GPUs also uses the 7-nanometer technology. Nvidia’s the H100, has 4-nanometer transistors. Nvidia’s hotly anticipated GH200 will have the same GPU as the H100, , but triple the memory capacity.
Conclusion
GenAI chips represent a quantum leap in AI processing, pushing the boundaries of what was once thought possible. As these specialized processors become more prevalent, we can expect to see unprecedented advancements in AI research, leading to smarter and more capable technologies. Their specialized nature positions them as key enablers for the next wave of advancements in AI and computing. Conventionally the expectation is that software companies with access to significant amounts of data, such as Microsoft and Google will benefit the most from GenAI. However, GenAI chips are poised to change that convention. Semiconductor companies producing GenAI chips could lay claim to a large slice of the financial gains from the GenAI enterprise.
