Adam-mini is a PyTorch optimizer that reduces Adam’s memory footprint by 50% while maintaining or exceeding performance. It partitions parameters into blocks based on Hessian structure and assigns a single learning rate per block, eliminating over 99.9% of redundant lr resources in the v state. The implementation is drop-in compatible with AdamW hyperparameters and supports transformer-specific configurations including multi-head attention parameters.