The GPU Audio SDK has been updated to support the latest NVIDIA and AMD Graphics Cards and we have more good news! Building on the support we received for Steven Atkinson’s NAM Module, we’re here to announce the second SDK module: Real-Time GPU Based Source Separation!
Showcased at 2025's Audio Developer Conference event in Bristol, we’ve delivered genuine real-time source separation utilizing deep learning for music demixing. Based on the HS TasNet which has been researched and investigated by L-Acoustics, this new GPU Audio SDK module provides a neural network adapted for real-time low-latency applications. Opening huge potential for remixing and noise-cancelling audio streams in real-time - for live shows and many other applications.
We’ve delivered real-time processing, something no one else has achieved.
The HS TasNet (Hybrid Spectrogram Time-domain Audio Separation Network) utilizes a combination of spectral and waveform domains which L-Acoustics state: “demonstrate the potential of efficient demixing for real-time low-latency music applications”. In their white paper, a latency of 23 ms is suggested, which we have developed to be just 512 samples with the GPU, even in its early stages.
Try the updated SDK for FREE by visiting https://gpu.audio/sdk
Some stats:
This is around a 12x performance boost with our Technology
These numbers are scalable on both single and multiple devices, with just a few milliseconds latency; no historical data (compared with Meta Demucs' v4), and the opportunity to rebuild using our SDK and achieve almost a 7x RT factor. Find out more about HS TasNet in the aforementioned white paper entitled: “Real-time Low-latency Music Source Separation Using Hybrid Spectrogram-TASNET“.
The updated SDK is available NOW for Windows and Mac on our Github page.
