Benchmarks
OpenNN Performance Benchmark
Compare OpenNN against other machine learning frameworks across training speed, inference, memory, deployment size, dependencies, and energy consumption.
Build
No dependency stack and a low-memory runtime.
32x
Less runtime memory
Peak RAM vs PyTorch & TensorFlow
Zero
Dependencies to install
Single-file, zero-install deploy
Learn
A compact, auditable C++ codebase and a concise API.
24x
Fewer lines of code
Native source vs PyTorch & TensorFlow
40%
Less code than PyTorch
Same Iris model, end to end
Load
Memory-mapped data handling for larger datasets.
2.7x
More data capacity
Same RAM, vs pandas + PyTorch/TF
Train
Faster training across dense, CNN, ResNet and transformer models.
1.79x
Faster GPU training
HIGGS, vs PyTorch
1.55x
Faster CPU training
HIGGS, vs PyTorch
1.3-1.9x
Faster CNN training
MNIST, vs PyTorch & TensorFlow
1.69x
Faster Transformer training
vs PyTorch
1.7x
Faster ResNet-50 training
CIFAR, vs PyTorch
1.35x
Faster dense GPU training
Rosenbrock MLP, vs PyTorch
Native
GPU training on Windows
Full CUDA path, unlike rivals
Validate
Matching accuracy, with better generalization.
On par
Numerical accuracy
Matches PyTorch & TensorFlow
Deploy
Tiny self-contained runtimes, fast startup and dependency-free export.
1.5x
Smaller GPU deployment
CNN CUDA build, vs PyTorch/TF
138x
Smaller CPU deployment
vs PyTorch & TensorFlow
7x
Lower startup latency
Time to first prediction
Runtime-free
Export to standalone code
Ship a model with no ML runtime
Operate
Efficient inference with lower energy use in production.
1.58x
Faster Transformer inference
GPU, vs PyTorch
ONNX-class
CPU inference speed
On par with ONNX Runtime