MegaTrain runs 120B LLM training on single GPU

MegaTrain shatters the hardware barrier for massive model fine-tuning by treating the GPU as a transient compute engine rather than persistent storage. This democratizes post-training research for teams without access to massive compute clusters.

• –Scales up to 120B parameter models on a single H200 by utilizing 1.5TB of host CPU memory
• –Achieves 1.84x higher throughput than DeepSpeed ZeRO-3 with CPU offloading when training 14B models
• –Eliminates the memory overhead of persistent autograd graphs by using dynamically bound stateless layer templates
• –Unlocks extreme 512k context window training for 7B models on a single GH200