Nvidia — The First To Undergo AI Powered Chip Reset
Ever since Artificial Intelligence (AI) debuted, the semiconductor industry has been either on tenterhooks for fear of being obsolete, or trying ways and means to ride on the wave without being held ransom by negligence-caused data security breaches. Nvidia the renowned chip player clearly is riding the waves.
Bloomberg reported Nvidia Corporation, the world’s most valuable chipmaker, forecast sales that surpassed analysts estimates, showing how booming demand for artificial intelligence (AI) processors has the potential to reshape the sector and sending shares to a record high. Sales in the three months ending in July will be about US$11 billion, Nvidia revealed on 24 May 2023. That shattered an average analyst estimate of US$7.18 billion.
“We’re seeing incredible orders to retool the world’s data centers,” Chief Executive Officer and co-founder Jensen Huang told analysts on a conference call. A trillion dollars of data center infrastructure will be upgraded to handle so-called accelerated computing, he said, letting them run generative AI tools such as ChatGPT. “The budget of a data center will shift very strongly to accelerated computing.”
The outlook shows that Nvidia is benefiting even more from the AI frenzy than thought possible. Under Huang, the company has positioned itself as the top provider of components for training AI software. That helped it weather a broader slowdown in technology spending.
Nvidia had outperformed stocks in the major indexes it trades on this year, turning it into the world’s fifth-largest publicly traded company by 24 May 2023’s close. It is now on the verge of joining Apple Inc. and others in the trillion-dollar club.
By market value, the chipmaker is eight times the size of Intel Corporation, a company that doubled Nvidia’s annual revenue last year. Revenue in Q1 2023 beat estimates by the widest margin in five years. The company’s forecast for sales this period is 53% higher than analysts projected, marking a record quarterly total.
“They may be in a unique position,” Sanford C. Bernstein analyst Stacy Rasgon said on Bloomberg Television. While the company doesn’t project individual unit revenue, the overall forecast implies a 75% surge in data center revenue, he said. “Is it a one-time thing or is this the new normal? I don’t know.”
An article from Kite Rocket cited advanced computer chips that semiconductor manufacturers design and fabricate power AI. The chip industry benefits from using those AI chips and software to optimise its semiconductor manufacturing processes. In that way, AI can facilitate incredible breakthroughs across the entire semiconductor manufacturing value chain.
The piece added that chip and AI industries are at an inflection point; the same conventional chips that have been used in PCs and servers have gotten us this far. To get to the next level in AI will take leading-edge chip and packaging solutions. When these new integrated systems reach the market, everything will be set in motion for AI to truly reach its fullest potential.
The Alpha, Not The Omega
Going back to basics, an AI chip is a type of semiconductor designed for AI specific applications and different from traditional chips. Firstly, the latter is incapable of handling large amounts of data required for AI applications.
AI chips are designed to work with neural networks, which are a key component of many AI applications. Traditional semiconductors are not as well-suited for working with neural networks.
A neural network is a method in artificial intelligence that teaches computers to process data in a way that is inspired by the human brain. It is a type of machine learning process, called deep learning, that uses interconnected nodes or neurons in a layered structure that resembles the human brain.
AI chips often incorporate specialised hardware that helps speed up the training and inference process. This includes dedicated neural network processors and hardware-accelerated maths libraries.
They are often designed to be power efficient, as many AI applications are deployed on mobile devices or other devices with limited power budgets. Therefore, it is a tall order for traditional chips to keep up with the influx of new computing functions which call for substantial volume of data and algorithms.
The change resulted in a design relook for AI chip to feature multiple microprocessors and accelerators that perform the different functions needed for the AI applications. Nvidia is singled out as one of the big potential winners in AI, following the viral success of the ChatGPT chatbot and other popular tools.
The company’s chips excel at parallel processing, which makes them well suited for training software by bombarding it with data. The growth also suggests that Nvidia is getting adequate supplies from manufacturing partners such as Taiwan Semiconductor Manufacturing Company (TSMC).
Nvidia Chief Financial Officer Colette Kress said the company has secured a “substantial” increase in supply of AI-related chips from its subcontractors for the second half of the year. Nvidia’s Huang argued that the use of the technology is only in its infancy and more tailored products for specific industries are needed.
He’s built online services and software tools to help encourage the broader adoption of AI outside of his big customers — cloud providers like Microsoft Corp. and Amazon.com Inc.’s AWS. As part of its Q2 2023 forecast, Nvidia predicted an adjusted gross margin of about 70%, above the 66.9% analysts were projecting.
The AI spending surge has boosted Nvidia’s data center unit, though a collapse in demand for personal computer components is still hammering its graphics chip business — and weighing on overall sales. Nvidia said revenue growth in its data center unit was driven by “strong demand from large consumer internet companies and cloud service providers.” Those customers are deploying its graphics chips to power generative AI and large language models, the company said.
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