  Neural graphics primitives, parameterized by fully connected neural networks,
can be costly to train and evaluate. We reduce this cost with a versatile new
input encoding that permits the use of a smaller network without sacrificing
quality, thus significantly reducing the number of floating point and memory
access operations: a small neural network is augmented by a multiresolution
hash table of trainable feature vectors whose values are optimized through
stochastic gradient descent. The multiresolution structure allows the network
to disambiguate hash collisions, making for a simple architecture that is
trivial to parallelize on modern GPUs. We leverage this parallelism by
implementing the whole system using fully-fused CUDA kernels with a focus on
minimizing wasted bandwidth and compute operations. We achieve a combined
speedup of several orders of magnitude, enabling training of high-quality
neural graphics primitives in a matter of seconds, and rendering in tens of
milliseconds at a resolution of ${1920\!\times\!1080}$.
