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Nvidia and DeepSeek

Nvidia and DeepSeek

DeepSeek challenged a tech industry consensus that to build bigger and better A.I. systems, companies would have to build bigger and more powerful data centers. It set off fears that companies might pullback on their spending with Nvidia. Since then, a new consensus has emerged that Nvidia will continue to benefit because it will become affordable for more companies to develop A.I. systems. An expanded field of A.I. business would create more customers for Nvidia’s expensive chips, not fewer, as…

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How did DeepSeek build its A.I. with less money?

How did DeepSeek build its A.I. with less money?

The Chinese start-up used several technological tricks, including a method called “mixture of experts” to significantly reduce the cost of building the technology. A.I. companies typically train their chatbots using supercomputers packed with 16,000 specialized chips or more. But DeepSeek said it needed only about 2,000. DeepSeek engineers needed only about $6 million in raw computing power, roughly one-tenth of what Meta spent in building its latest A.I. technology. What exactly did DeepSeek do? How are A.I. technologies built? Companies…

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DeepSeek just Exposed the Rot at the Core of the AI Industry

DeepSeek just Exposed the Rot at the Core of the AI Industry

DeepSeek made two critical changes. Firstly. the architecture. OpenAI uses an AI architecture known as “fully dense”. This basically means that the architecture is comprised of a single, vast network that processes every request with all its parameters and data points. This is incredibly computationally dense, but the idea is that it can make it more capable in a broader application. DeepSeek is instead much more picky and uses a “mixture of experts” architecture. In this approach, the AI is…

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DeepSeek

DeepSeek

DeepSeek’s breakthrough on cost challenges the “bigger is better” narrative that has driven the A.I. arms race in recent years by showing that relatively small models, when trained properly, can match or exceed the performance of much bigger models. That, in turn, means that A.I. companies may be able to achieve very powerful capabilities with far less investment than previously thought. And it suggests that we may soon see a flood of investment into smaller A.I. start-ups, and much more…

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