Spectra is a drop-in optimizer replacement for AdamW that shifts gradient optimization to the frequency domain and factorizes second-moment variance, cutting optimizer state memory by 50% while keeping full 32-bit floating-point stability.
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Replace torch.optim.AdamW with Spectra in one line and leave the training loop unchanged.
The project benchmarks optimizer state at 4130 MB against 8256 MB for standard AdamW on a 12-layer transformer block.
Training logs for the benchmark runs are published on Weights and Biases.
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