  We present NeRF-XL, a principled method for distributing Neural Radiance
Fields (NeRFs) across multiple GPUs, thus enabling the training and rendering
of NeRFs with an arbitrarily large capacity. We begin by revisiting existing
multi-GPU approaches, which decompose large scenes into multiple independently
trained NeRFs, and identify several fundamental issues with these methods that
hinder improvements in reconstruction quality as additional computational
resources (GPUs) are used in training. NeRF-XL remedies these issues and
enables the training and rendering of NeRFs with an arbitrary number of
parameters by simply using more hardware. At the core of our method lies a
novel distributed training and rendering formulation, which is mathematically
equivalent to the classic single-GPU case and minimizes communication between
GPUs. By unlocking NeRFs with arbitrarily large parameter counts, our approach
is the first to reveal multi-GPU scaling laws for NeRFs, showing improvements
in reconstruction quality with larger parameter counts and speed improvements
with more GPUs. We demonstrate the effectiveness of NeRF-XL on a wide variety
of datasets, including the largest open-source dataset to date, MatrixCity,
containing 258K images covering a 25km^2 city area.
