  Neural networks have shown great potential in compressing volume data for
visualization. However, due to the high cost of training and inference, such
volumetric neural representations have thus far only been applied to offline
data processing and non-interactive rendering. In this paper, we demonstrate
that by simultaneously leveraging modern GPU tensor cores, a native CUDA neural
network framework, and a well-designed rendering algorithm with macro-cell
acceleration, we can interactively ray trace volumetric neural representations
(10-60fps). Our neural representations are also high-fidelity (PSNR > 30dB) and
compact (10-1000x smaller). Additionally, we show that it is possible to fit
the entire training step inside a rendering loop and skip the pre-training
process completely. To support extreme-scale volume data, we also develop an
efficient out-of-core training strategy, which allows our volumetric neural
representation training to potentially scale up to terascale using only an
NVIDIA RTX 3090 workstation.
