US microchip export restrictions imposed last year to freeze the development of China’s supercomputers used to develop nuclear weapons and artificial intelligence systems like ChatGPT have had minimal impact on China’s tech sector. do not have.
The rule restricted the shipment of chips from Nvidia Corp and Advanced Micro Devices Inc, which have become the global tech industry standard for developing chatbots and other AI systems. However, Nvidia has created a variant of the chip for the Chinese market that has been slowed down to match US regulations. Industry experts told Reuters that the latest Nvidia H800, announced in March, will likely take 10% to 30% longer to perform some AI tasks, making it the fastest U.S.-made chip from Nvidia. He said the cost could double compared to .
Even the slowdown in Nvidia chips shows improvement for the Chinese company. Tencent Holdings, one of China’s biggest tech companies, said in April that his H800-powered system from Nvidia cut the time it takes to train its largest AI system by more than half from 11 days to 4. presumed to be Charlie Chai, an analyst at his Shanghai-based 86Research, said:
The back-and-forth between government and industry exposes the U.S. challenge to slow China’s progress in high-tech without hurting U.S. companies. Part of the U.S. strategy in setting the rules was to avoid shocking China to the point of ditching U.S. chips altogether and doubling down on its own chip development efforts.
“They have to draw the line somewhere, and wherever they do, they’re going to face the challenge of how to degrade China’s capabilities over time, rather than creating chaos right away. No,” said a chip industry executive who wished to remain anonymous. To discuss private discussions with regulators. Export restrictions he has two parts. The first caps the chip’s ability to compute highly accurate numbers, a measure designed to limit the supercomputers available for military research. Chip industry sources say it was an effective move.
But computing highly accurate numbers is less relevant for AI tasks like large-scale language models, where the amount of data a chip can handle is more important. Nvidia has sold his H800 to some of China’s biggest tech companies, including Tencent, Alibaba Group Holding Ltd and Baidu Inc, but has yet to begin mass shipments of the chip.
In a statement last week, Nvidia said, “The government does not seek to harm competition or U.S. industry and does not allow U.S. companies to supply products for commercial activities, such as providing cloud services to consumers. there are,” he said. China is an important customer for U.S. technology, he added.
Nvidia said last week, “October export controls require us to create products that widen the gap between the two markets. “This gap will grow rapidly over time as training requirements continue to double year over year,” Nvidia chief scientist Bill Dally said in a separate statement this week. 6 to 12 months.”
A spokeswoman for the Bureau of Industry and Security, the U.S. Department of Commerce department that oversees regulations, did not return requests for comment.
A second limitation in the United States concerns chip-to-chip transfer speeds that affect AI. The model behind technology such as ChatGPT is too big for him to fit on one chip. Instead, they have to be distributed over many chips (sometimes thousands at a time), all of which need to communicate with each other. Nvidia hasn’t revealed performance details for his China-only H800 chip, but specs confirmed by Reuters show speeds of 400 gigabytes per second between chips and Nvidia’s flagship H100. That’s less than half the chip’s maximum speed of 900 gigabytes per second. Available outside China.
Some in the AI industry still think it’s fast enough. Naveen Rao, CEO of a startup called MosaicML, who specializes in helping his AI models run better on limited hardware, said he experienced a 10-30% slowdown in systems. I estimate that. “There are ways around all of this algorithmically,” he said. “I don’t think this will be the boundary for very long – ten years or so.”
Money helps. Even a Chinese chip that takes twice as long to complete an AI training task can complete the task better than a faster U.S. chip: “At that point, training would cost him $20 million instead of his $10 million.” ,” said an industry source who requested anonymity because of the agreement with the partner. “Is that terrible? Yes it is. But does that mean this is not possible for Alibaba or Baidu? No, it doesn’t matter.”
Additionally, AI researchers are looking to slim down the large systems they have built to reduce the cost of training products like ChatGPT and other processes. These require fewer chips, reduce chip-to-chip communication, and reduce the impact of US speed limits. Two years ago, the industry thought his AI models were getting bigger and bigger, said Cade Daniel, a software engineer at Anyscale.
“If that’s still true today, this export restriction will have a bigger impact,” Daniel said.
(This article is not edited by Devdiscourse staff and is auto-generated from a syndicated feed.)
