The recent MLPerf Training report underscores a significant shift in AI technology, emphasizing the critical role of connections between chips in training neural networks. With the latest tests showing up to 8,192 GPU chips in networks, the importance of how these chips communicate is greater than ever. David Kanter of MLCommons highlighted that as AI systems scale from hundreds of GPUs to potentially millions, factors like network configuration and problem-mapping algorithms will be pivotal. This evolution marks a departure from solely focusing on chip speeds to a more integrated approach.
The latest MLPerf Training results indicate AI systems are becoming increasingly dependent on the interconnections between chips, not just the speed of individual chips.
As AI systems scale up from hundreds to potentially millions of GPU chips, the architecture and mapping algorithms of the networks gain crucial importance.
Collection
[
|
...
]