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.
Baseline RAM and GPU-ready VRAM
OpenNN vs PyTorch & TensorFlow · baseline footprint
Dependencies & install friction
OpenNN vs PyTorch & TensorFlow · install requirements
Learn
A compact, auditable C++ codebase and a concise API.
Source lines of code
OpenNN vs PyTorch & TensorFlow · native source
Iris API lines of code
OpenNN vs PyTorch & TensorFlow · same Iris model
Load
Memory-mapped data handling for larger datasets.
Train
Faster training across dense, CNN, ResNet and transformer models.
GPU HIGGS dense training
OpenNN vs PyTorch & TensorFlow · HIGGS
CPU HIGGS dense training
OpenNN vs PyTorch & TensorFlow · 3-run median, 0.7% dispersion
GPU Transformer training
OpenNN vs PyTorch & TensorFlow · GPU training
GPU ResNet-50 training
OpenNN vs PyTorch & TensorFlow · CIFAR-10
GPU on Windows
OpenNN vs PyTorch & TensorFlow · native CUDA path
Optimize
Precision-aware acceleration across GPU training and inference.
GPU fp32 vs bf16 precision sweep
OpenNN bf16 vs fp32 · best speedup across four workloads
Validate
Matching accuracy, with better generalization.
Deploy
Tiny self-contained runtimes, fast startup and dependency-free export.
Deployment size on GPU (CNN)
OpenNN vs PyTorch & TensorFlow · CNN CUDA build
Deployment size on CPU
OpenNN vs PyTorch & TensorFlow · CPU package
Startup latency
OpenNN vs PyTorch & TensorFlow · first prediction
Model export to standalone code
OpenNN vs PyTorch & TensorFlow · standalone artifact
Operate
Efficient inference with lower energy use in production.
GPU Transformer inference
OpenNN vs PyTorch & TensorFlow · up to 680.4k tok/s
GPU HIGGS dense inference
OpenNN vs PyTorch & TensorFlow · 15.34M/34.84M samples/s
CPU HIGGS dense inference
OpenNN vs PyTorch & TensorFlow · 441.5k samples/s
GPU ResNet-50 inference
OpenNN vs PyTorch & TensorFlow · fp32/bf16, 5 runs