Inside the hunt for AI chips everyone wants Nvidia’s chips

Kabari99-The most sought-after resource in the tech industry right now isn’t a specific type of engineer. It’s not even money. It’s an AI chip made by Nvidia called the H100.

Securing these GPUs is “considerably harder to get than drugs,” Elon Musk has said.

“Who’s getting how many H100s and when is top gossip of the valley rn,” OpenAI’s Andrej Karpathy posted last week.

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I’ve spent this week talking with sources throughout the AI industry,

from the big AI labs to cloud providers and small startups, and come away with this everyone

is operating under the assumption that H100s will be nearly impossible to get through at least the first half of next year.

The lead time for new orders, if you can get them, is roughly six months, or an eternity in the AI space.

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Meanwhile,

the cloud providers are just starting to make their H100s widely available

and charging an arm and a leg for the limited capacity they have.

For the most part, these hosting providers also require extremely costly, lengthy upfront commitments.

With the emergence of artificial intelligence and machine learning, a wide array of advanced chips

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and hardware are being developed to deal with complex network processes.

Artificial intelligence chips consist of AI-specialized graphics processing units (GPUs),

application-specific integrated circuits (ASICs), and field-programmable gate arrays (FPGAs).

contains general information about graphics processing units (GPUs) and video cards from Nvidia, based on official specifications.

In addition some Nvidia motherboards come with integrated onboard GPUs. Limited/Special/Collectors’ Editions or AIB versions are not included.

AI chips are a thousand times faster

And more efficient than general-purpose CPUs for the training & inference of AI algorithms.

However, AI chips are similar to general-purpose CPUs in terms

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of gaining speed and efficiency by integrating numerous tiny transistors.

Smaller transistors are preferable because

they run faster and consume less energy than larger transistors.

The more the number of transistors in an AI chip,

the more is their ability to deliver computational power.

On the other hand, AI chips, unlike CPUs,

including AI optimized design features that speed up the calculations needed by AI algorithms.

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Recently,

Wafer Scale Engine 2 (WSE-2) chip has been referred to as the largest AI processor as it involves 2.6 trillion transistors,

40 GB memory, and 8,50,000 cores. WSE-2 with such specifications stands over GPU

or system-on-chip competitors with 1000 times more memory and 123 times more cores.

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According to a report published by Allied Market Research,

the global artificial intelligence chip market size is anticipated to reach $8.02 billion with a considerable CAGR from 2021 to 2030.

The Asia-Pacific region is expected to grow at the highest rate during the forecasted period.

A company named Nvidia is currently holding the crown in the global AI chip market.

The race to make faster and more efficient AI chips has made key market players innovate and launch new products.

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Moreover, an enterprise, Syntiant,

Is looking forward to launching NDP120 Neural Decision Processor which is expected to bring low power edge devices to the next level.

This chip is useful for mobile phones, laptops, earbuds, smart wearables, smart speakers,

security devices, and, smart home applications. It includes support for up to seven audio streams.

And then, the emergence of autonomous robotics is creating lucrative opportunities for the AI chip industry players,

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which in turn, is boosting the growth of the global AI chip market to a great extent. Several economies,

especially the U.S., have witnessed significant growth in tech AI start-ups in the past few years.

Here, it is worth mentioning that with such continuous innovations by key market players,

the global artificial intelligence chip market is definitely going to assemble huge prospects & exponential growth in the near future.

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