# Slimmable NAM: Neural Amp Models with adjustable runtime computational cost

arXiv: https://arxiv.org/abs/2511.07470 · HTML: https://mlsanity.com/paper/2511.07470/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 10 references that are in the map.
- First posted on arXiv 8 Nov 2025.

## Builds on (most cited first, 6 of 6)

- [WaveNet: A Generative Model for Raw Audio](https://mlsanity.com/paper/1609.03499/) (2016)
- [Once-for-All: Train One Network and Specialize it for Efficient Deployment](https://mlsanity.com/paper/1908.09791/) (2019)
- [Slimmable Neural Networks](https://mlsanity.com/paper/1812.08928/) (2018)
- [Model compression via distillation and quantization](https://mlsanity.com/paper/1802.05668/) (2018)
- [Dynamic Slimmable Networks for Efficient Speech Separation](https://mlsanity.com/paper/2507.06179/) (2025)
- [Binary Early-Exit Network for Adaptive Inference on Low-Resource Devices](https://mlsanity.com/paper/2206.09029/) (2022)

## Abstract (arXiv)

This work demonstrates "slimmable Neural Amp Models", whose size and computational cost can be changed without additional training and with negligible computational overhead, enabling musicians to easily trade off between the accuracy and compute of the models they are using. The method's performance is quantified against commonly-used baselines, and a real-time demonstration of the model in an audio effect plug-in is developed.