Optimizers Comparison
Compare PyTorch optimizers: Adam, AdamW, SGD, RMSprop, and more. Key parameters, when to use, memory requirements, and best practices for each optimizer.
Built by Michael Lip
Frequently Asked Questions
Which optimizer should I use?
AdamW for transformers and NLP. SGD with momentum for CNNs (often better final accuracy). Adam for quick experiments and most general use. RAdam or Adafactor for stability without learning rate warmup.
What is the difference between Adam and AdamW?
Adam applies weight decay to the gradient before computing adaptive learning rates, which couples regularization with adaptation. AdamW decouples weight decay from the gradient update, applying it directly to the parameters. AdamW produces better generalization, especially with learning rate schedules.
How much memory do optimizers use?
SGD with momentum: 1x parameters (momentum buffer). Adam/AdamW: 2x parameters (first and second moment estimates). So Adam uses 2x more optimizer memory than SGD. For a 1B parameter model in float32, that's 4GB (SGD) vs 8GB (Adam) of optimizer states.
About This Tool
This tool is part of HeyTensor, a free suite of PyTorch and deep learning utilities. All calculations run entirely in your browser — no data is sent to any server. The source code is open on GitHub.
Contact
HeyTensor is built and maintained by Michael Lip. For questions or feedback, email [email protected].