Explore how Microsoft’s ZeRO optimizer enables training of trillion-parameter AI models by eliminating memory redundancy across GPUs, dramatically improving distributed training efficiency.
Latest Posts
The Journey to Super Alignment: From Weak to Strong Generalization in Large Language Models
Explore the critical challenge of AI alignment, from ensuring basic rule-following to developing systems that robustly generalize human values across novel situations as models become increasingly capable.
Large-Scale AI Infrastructure on Kubernetes: Scaling Training for Modern LLMs
Explore the architectural patterns and engineering considerations for building Kubernetes-based infrastructure capable of training massive AI models with billions to trillions of parameters.
FlashAttention: Revolutionizing Transformer Efficiency
Explore how FlashAttention dramatically improves transformer performance by optimizing memory access patterns, enabling faster training and inference while supporting longer sequences.
Understanding the Transformer Architecture: Self-Attention and Beyond
Dive deep into the self-attention mechanism that powers modern language models, exploring how the Transformer architecture revolutionized AI through its innovative approach to processing sequential data.