Tiny CUDA Neural Networks

This is a small, self-contained framework for training and querying neural networks. Most notably, it contains a lightning fast “fully fused” multi-layer perceptron (technical paper), a versatile multiresolution hash encoding (technical paper), as well as support for various other input encodings, losses, and optimizers.


Fully fused networks vs. TensorFlow v2.5.0 w/ XLA. Measured on 64 (solid line) and 128 (dashed line) neurons wide multi-layer perceptrons on an RTX 3090. Generated by benchmarks/bench_ours.cu and benchmarks/bench_tensorflow.py.


Tiny CUDA neural networks have a simple C++/CUDA API:

#include <tiny-cuda-nn/common.h>

// Configure the model
nlohmann::json config = {
	{"loss", {
		{"otype", "L2"}
	{"optimizer", {
		{"otype", "Adam"},
		{"learning_rate", 1e-3},
	{"encoding", {
		{"otype", "OneBlob"},
		{"n_bins", 32},
	{"network", {
		{"otype", "FullyFusedMLP"},
		{"n_neurons", 64},
		{"n_hidden_layers", 5},
		{"activation", "ReLU"},
		{"output_activation", "None"},

using namespace tcnn;

auto model = create_from_config(n_input_dims, n_output_dims, config);

// Train the model
GPUMatrix<float> training_batch_inputs(n_input_dims, batch_size);
GPUMatrix<float> training_batch_targets(n_output_dims, batch_size);

for (int i = 0; i < n_training_steps; ++i) {
	generate_training_batch(&training_batch_inputs, &training_batch_targets); // <-- your code

	float loss;
	model.trainer->training_step(training_batch_inputs, training_batch_targets, &loss);
	std::cout << "iteration=" << i << " loss=" << loss << std::endl;

// Use the model
GPUMatrix<float> inference_inputs(n_input_dims, batch_size);
generate_inputs(&inference_inputs); // <-- your code

GPUMatrix<float> inference_outputs(n_output_dims, batch_size);
model.network->inference(inference_inputs, inference_outputs);

Example: learning a 2D image

We provide a sample application where an image function (x,y) -> (R,G,B) is learned. It can be run via

tiny-cuda-nn/build> ./mlp_learning_an_image ../data/images/albert.exr ../data/config.json

producing an image every 1000 training steps. Each 1000 steps should take roughly 0.8 seconds with the default configuration on an RTX 3090.

Learned image after 1,000 steps Learned image after 10,000 steps Reference image
1,000 steps 10,000 steps reference


  • CUDA v10.2 or higher.
  • CMake v3.18 or higher.
  • A C++14 capable compiler.
  • A high-end NVIDIA GPU that supports TensorCores and has a large amount of shared memory. The framework was tested primarily with an RTX 3090.
  • The fully fused MLP component of this framework requires a very large amount of shared memory in its default configuration. It will likely only work on an RTX 3090, an RTX 2080 Ti, or high-end enterprise GPUs. Lower end cards must reduce the n_neurons parameter or use the CutlassMLP (better compatibility but slower) instead.


Begin by cloning this repository and all its submodules using the following command:

$ git clone --recursive https://github.com/nvlabs/tiny-cuda-nn
$ cd tiny-cuda-nn

Then, use CMake to generate build files:

tiny-cuda-nn$ mkdir build
tiny-cuda-nn$ cd build
tiny-cuda-nn/build$ cmake ..

The last step differs by operating system.

  • Windows: open tiny-cuda-nn/build/tiny-cuda-nn.sln in Visual Studio and click the “Build” button.
  • Linux: run the command
    tiny-cuda-nn/build$ make -j


The following is a summary of all components of this framework that are currently released. Please consult the JSON documentation for how to configure them.

Fully fused MLP src/fully_fused_mlp.cu Lightning fast implementation of small multi-layer perceptrons (MLPs).
CUTLASS MLP src/cutlass_mlp.cu MLP based on CUTLASS‘ GEMM routines. Slower than fully-fused, but handles larger networks and still is reasonably fast.
CUTLASS ResNet src/cutlass_resnet.cu Fully connected residual network based on CUTLASS’ GEMM routines.
Input encodings    
Composite include/tiny-cuda-nn/encodings/composite.h Allows composing multiple encodings. Can be, for example, used to assemble the Neural Radiance Caching encoding [Müller et al. 2021].
Frequency include/tiny-cuda-nn/encodings/frequency.h NeRF’s [Mildenhall et al. 2020] positional encoding applied equally to all dimensions.
Grid include/tiny-cuda-nn/encodings/grid.h Encoding based on trainable multiresolution grids. Used for Instant Neural Graphics Primitives [Müller et al. 2022]. The grids can be backed by hashtables, dense storage, or tiled storage.
Identity include/tiny-cuda-nn/encodings/identity.h Leaves values untouched.
Oneblob include/tiny-cuda-nn/encodings/oneblob.h From Neural Importance Sampling [Müller et al. 2019] and Neural Control Variates [Müller et al. 2020].
SphericalHarmonics include/tiny-cuda-nn/encodings/spherical_harmonics.h A frequency-space encoding that is more suitable to direction vectors than component-wise ones.
TriangleWave include/tiny-cuda-nn/encodings/triangle_wave.h Low-cost alternative to the NeRF’s encoding. Used in Neural Radiance Caching [Müller et al. 2021].
L1 include/tiny-cuda-nn/losses/l1.h Standard L1 loss.
Relative L1 include/tiny-cuda-nn/losses/l1.h Relative L1 loss normalized by the network prediction.
MAPE include/tiny-cuda-nn/losses/mape.h Mean absolute percentage error (MAPE). The same as Relative L1, but normalized by the target.
SMAPE include/tiny-cuda-nn/losses/smape.h Symmetric mean absolute percentage error (SMAPE). The same as Relative L1, but normalized by the mean of the prediction and the target.
L2 include/tiny-cuda-nn/losses/l2.h Standard L2 loss.
Relative L2 include/tiny-cuda-nn/losses/relative_l2.h Relative L2 loss normalized by the network prediction [Lehtinen et al. 2018].
Relative L2 Luminance include/tiny-cuda-nn/losses/relative_l2_luminance.h Same as above, but normalized by the luminance of the network prediction. Only applicable when network prediction is RGB. Used in Neural Radiance Caching [Müller et al. 2021].
Cross Entropy include/tiny-cuda-nn/losses/cross_entropy.h Standard cross entropy loss. Only applicable when the network prediction is a PDF.
Variance include/tiny-cuda-nn/losses/variance_is.h Standard variance loss. Only applicable when the network prediction is a PDF.
Adam include/tiny-cuda-nn/optimizers/adam.h Implementation of Adam [Kingma and Ba 2014], generalized to AdaBound [Luo et al. 2019].
Novograd include/tiny-cuda-nn/optimizers/lookahead.h Implementation of Novograd [Ginsburg et al. 2019].
SGD include/tiny-cuda-nn/optimizers/sgd.h Standard stochastic gradient descent (SGD).
Shampoo include/tiny-cuda-nn/optimizers/shampoo.h Implementation of the 2nd order Shampoo optimizer [Gupta et al. 2018] with home-grown optimizations as well as those by Anil et al. [2020].
Average include/tiny-cuda-nn/optimizers/average.h Wraps another optimizer and computes a linear average of the weights over the last N iterations. The average is used for inference only (does not feed back into training).
Batched include/tiny-cuda-nn/optimizers/batched.h Wraps another optimizer, invoking the nested optimizer once every N steps on the averaged gradient. Has the same effect as increasing the batch size but requires only a constant amount of memory.
EMA include/tiny-cuda-nn/optimizers/average.h Wraps another optimizer and computes an exponential moving average of the weights. The average is used for inference only (does not feed back into training).
Exponential Decay include/tiny-cuda-nn/optimizers/exponential_decay.h Wraps another optimizer and performs piecewise-constant exponential learning-rate decay.
Lookahead include/tiny-cuda-nn/optimizers/lookahead.h Wraps another optimizer, implementing the lookahead algorithm [Zhang et al. 2019].

License and Citation

This framework is licensed under the BSD 3-clause license. Please see LICENSE.txt for details.

If you use it in your research, we would appreciate a citation via

    Author = {Thomas M\"uller},
    Year = {2021},
    Note = {https://github.com/nvlabs/tiny-cuda-nn},
    Title = {Tiny {CUDA} Neural Network Framework}

For business inquiries, please visit our website and submit the form: NVIDIA Research Licensing


This framework powers the following publications:

Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
Thomas Müller, Alex Evans, Christoph Schied, Alexander Keller
arXiv:2201.05989 [cs.CV], Jan 2022
Website ] [ Paper ] [ Code ] [ Video ] [ BibTeX ]

Real-time Neural Radiance Caching for Path Tracing
Thomas Müller, Fabrice Rousselle, Jan Novák, Alexander Keller
ACM Transactions on Graphics (Proceedings of SIGGRAPH), vol. 40, no. 4, pp. 36:1–36:16, Aug 2021
Paper ] [ GTC talk ] [ Video ] [ Interactive Results Viewer ] [ BibTeX ]

Extracting Triangular 3D Models, Materials, and Lighting From Images
Jacob Munkberg, Jon Hasselgren, Tianchang Shen, Jun Gao, Wenzheng Chen, Alex Evans, Thomas Müller, Sanja Fidler
arXiv:2111.12503 [cs.CV], Nov 2021
Website ] [ Paper ] [ Video ] [ BibTeX ]


Special thanks go to the NRC authors for helpful discussions and to Nikolaus Binder for providing part of the infrastructure of this framework, as well as for help with utilizing TensorCores from within CUDA.


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