Foxconn Reports 2.48 Times Faster Model Training on DGX SuperPOD
Photo: TokenPost
Foxconn says its DGX SuperPOD trained 120-billion-parameter models 2.48 times faster than its H100 baseline, while supporting AI work across the electronics maker’s factory operations. Foxconn’s benchmark used DGX GB200 systems built on NVIDIA’s Blackwell architecture. The company compared training performance with an H100-based baseline and counted one trillion tokens processed over 12 days using training-job telemetry. “By utilizing the NVIDIA Blackwell-based DGX SuperPOD, we achieved a 2.48x training speedup, enabling us to process one trillion tokens in just 12 days to power our global AI factory,” said Yung-Hui Li, founding director of AI Research Center at Hon Hai Research…
Source: TokenPost
