OpenNN Performance Benchmark
Compare OpenNN against other frameworks
1.79x Faster GPU Training Speed on HIGGS
Performance Benchmark
OpenNN
1.01M samples/s
PyTorch CUDA Graphs
563k samples/s
OpenNN is 1.79× faster
Higher is better · RTX 4080 · batch size 100
1.55x Faster CPU Training Speed on HIGGS
Performance Benchmark
OpenNN
72k samples/s
PyTorch CPU
46k samples/s
OpenNN is 1.55× faster
Higher is better · Intel Core i9-12900K · MKL
42% Lower Energy Consumption
Save power, save money
Neural Designer
2.6 kWh
42% less energy than TensorFlow
TensorFlow
4.5 kWh
Lower is better · GPU training benchmark
1.27x Better Generalization
Smarter model selection
Neural Designer
MSE 0.0174
PyTorch
MSE 0.0221
Neural Designer is 1.27× more precise
TensorFlow
MSE 0.0333
Neural Designer is 1.91× more precise
Bars show relative precision from minimum MSE · higher is better
Similar Numerical Accuracy
Accuracy without compromise
OpenNN
MSE 0.0116 · R² 0.9879
PyTorch
MSE 0.0114 · R² 0.9882
TensorFlow
MSE 0.0129 · R² 0.9867
Lower MSE is better · Accuracy is essentially on par
1.5x less GPU CNN Deployment Size
Smaller CUDA deployment
OpenNN
~1.3 GB
ONNX Runtime
~2.0 GB
≈1.5× larger than OpenNN
PyTorch
~5.0 GB
≈4× larger than OpenNN
TensorFlow
~6.2 GB
≈5× larger than OpenNN
138x less CPU Deployment Size
Tiny CPU deployment
OpenNN
3.2 MB
PyTorch
442 MB
≈138× larger than OpenNN
TensorFlow
752 MB
≈235× larger than OpenNN
7x less Startup Latency
Faster time to first prediction
OpenNN
36 ms
ONNX Runtime
237 ms
≈7× slower than OpenNN
PyTorch
1,005 ms
≈28× slower than OpenNN
TensorFlow
1,685 ms
≈47× slower than OpenNN
24x less Lines Of Code
Smaller native codebase
OpenNN
34.9k
PyTorch
834k
24× more than OpenNN
TensorFlow
1.79M
51× more than OpenNN
32x less CPU Memory
Lower runtime memory
OpenNN
9 MB
PyTorch
295 MB
≈32× more than OpenNN
TensorFlow
521 MB
≈56× more than OpenNN
Zero Dependencies
Zero-install deployment
OpenNN
0 packages
Single executable · no Python required
ONNX Runtime
6 packages
Requires Python interpreter
PyTorch
12 packages
Requires Python + package tree
TensorFlow
33 packages
Largest dependency footprint
Model export to standalone code
Runtime-free model export
OpenNN
Standalone source
YES
C · Python · JavaScript · PHP
PyTorch
Runtime required
NO
TorchScript / ONNX need libtorch or ONNX Runtime
TensorFlow
Runtime required
NO
SavedModel / TFLite need TF or TFLite runtime
OpenNN C export: ~231 KB source · ~236 KB binary
2.7x More Data Capacity
Train bigger DataSets
Maximum samples under 8 GB memory
OpenNN handles 2.7× more data in this benchmark.