This episode explores the challenges and triumphs of developing GPT-4.5, a large language model. Against the backdrop of exceeding expectations for GPT-4.5's performance, the discussion delves into the extensive research and development process. More significantly, the panel reveals that scaling up from 10,000 to 100,000 GPUs introduced unforeseen complexities, including increased infrastructure failures and the need for multi-cluster training. For instance, a seemingly minor bug in the torch.sum function caused numerous seemingly unrelated issues, highlighting the intricate nature of large-scale model training. The conversation then pivots to future scaling, emphasizing the need for data efficiency and system-level fault tolerance. Ultimately, the panelists express optimism about future advancements, suggesting that while current methods are far from human-level data efficiency, algorithmic innovations and a shift from compute- to data-constrained environments hold promise for the next generation of large language models. This signifies a crucial shift in the AI landscape, moving beyond simply increasing compute power to focus on more efficient algorithms and data utilization.
