MegaTrain revolutionizes large language model training by enabling full-precision training of models exceeding 100 billion parameters on a single GPU. Developed by Zhengqing Yuan, this memory-centric system stores parameters and optimizer states in host CPU memory, freeing GPUs to function as transient compute engines. Unlike traditional GPU-centric approaches, MegaTrain streams parameters per layer, minimizing persistent device state and eliminating autograd graph overhead.

Key optimizations include pipelined double-buffering to overlap computation and gradient offloading across CUDA streams, and stateless layer templates that bind weights dynamically as they stream in. On a single H200 GPU with 1.5TB host memory, MegaTrain reliably trains models up to 120B parameters, achieving 1.84x training throughput over DeepSpeed ZeRO-3 with CPU offloading for 14B models. This breakthrough makes cutting-edge LLM training more accessible, potentially reducing infrastructure costs for research and development.