dailyai.report

Parameter count alone no longer predicts model performance, according to Jie Tang. The Z.ai CEO argues that data volume, compute allocation, and deployment conditions now dictate utility. This shifts focus toward post-training scaling laws over raw size. Practitioners must prioritize data quality and inference efficiency over chasing higher parameter counts to optimize model output.