San Francisco, June 27 (Reuters) - MLCommons, a group that develops benchmark tests for artificial intelligence (AI) technology, on Tuesday unveiled results for a new test that determines system ...

The MLPerf Training team recently submitted MLPerf R-Gat to the Illinois Graph Benchmark (IGB) leaderboard which helps the industry keep track of the state of the art for GNN models, encouraging ...

Training AI models is a whole lot faster in 2023, according to the results from the MLPerf Training 3.1 benchmark released today. The pace of innovation in the generative AI space is breathtaking to ...

For those who enjoy rooting for the underdog, the latest MLPerf benchmark results will disappoint: Nvidia’s GPUs have dominated the competition yet again. This includes chart-topping performance on ...

MLCommons, a group that develops benchmarks for AI technology training algorithms, revealed the results for a new test that determines system speeds for training algorithms specifically used for the ...

Depending on the hardware you're using, training a large language model of any significant size can take weeks, months, even years to complete. That's no way to do business — nobody has the ...

As the hype and momentum behind generative AI continue to grow, so too does the performance of the underlying systems that enable machine learning (ML) training. The big new inclusion with MLPerf ...

Today, MLCommons announced new results from two MLPerf benchmark suites: the MLPerf Training v3.1 suite, which measures the performance of training machine learning models; and the MLPerf HPC v.3.0 ...

Generative-AI companies have been selling a narrative of unprecedented, endless progress. Just last week, OpenAI introduced GPT-4.5 as its “largest and best model for chat yet.” Earlier in February, ...