The post Nvidia’s new Blackwell chips trained Meta’s large Llama 3.1 model in just 27 minutes appeared on BitcoinEthereumNews.com.

Nvidia’s new Blackwell chips are changing how quickly artificial intelligence systems can be trained. In the latest round of benchmarking results released on Wednesday by MLCommons, a nonprofit group that tracks and compares the capabilities of AI chips, the Blackwell architecture programmed by Nvidia set records. When tested with Meta’s open-source Llama 3.1 405B model, one of its biggest and most complex AI models, training was finished in only 27 minutes using Blackwell chips. This was done with only 2,496 Blackwell GPUs, an order of magnitude less than what it would have taken with Nvidia’s previous Hopper chips. In contrast, previous designs used over three times as many Hopper GPUs to deliver equivalent performance. By the chip, Blackwell was more than twice as speedy, which was a huge jump in convergence efficiency. That kind of performance boost could translate into major time and cost savings for organizations training trillion-parameter models. These results are believed to be the first MLCommons benchmarks for training models at these extreme scales and provide a real-world measurement of how well chips handle the most demanding AI workloads. CoreWeave, Nvidia drive smarter AI scaling Not only were the results a victory for Nvidia, but they also highlighted the work of CoreWeave, a cloud infrastructure company that partnered on the tests. In a press conference, CoreWeave Chief Product Officer Chetan Kapoor pointed out a general direction that increasingly made sense in the industry: away from large, homogeneous blocks of tens of thousands of GPUs. Rather than building a single, massive, monolithic computing system, companies are now looking at smaller, interconnected subsets that can manage massive model training more efficiently and with better scaling. Kapoor said that with such a technique, developers can continue scaling up or cutting down the time required to train extremely large models with…

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Filed under: News - @ June 5, 2025 4:24 am