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Model export to standalone code: OpenNN vs PyTorch vs TensorFlow
OpenNN exports a trained model as standalone source code that runs with no ML runtime, unlike PyTorch and TensorFlow. Ideal for firmware and embedded targets.
OpenNN
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Benchmarks, platform notes, and technical articles from the OpenNN team.
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Source lines of code: OpenNN vs PyTorch vs TensorFlow
Native source code compared: OpenNN is about 35K lines of C/C++ against 834K for PyTorch and 1.79M for TensorFlow, a smaller and more auditable library layer.
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Baseline RAM and GPU-ready VRAM: OpenNN vs PyTorch vs TensorFlow
OpenNN uses 240 MB of baseline RAM and 250 MB of GPU-ready VRAM, the lowest footprint in this matched comparison with PyTorch and TensorFlow.
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Deployment size on CPU: OpenNN vs PyTorch vs TensorFlow
CPU deployment size compared: OpenNN ships a tiny native artifact, vs the hundreds of megabytes PyTorch and TensorFlow need on space-limited edge targets.
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Deployment size on GPU (CNN): OpenNN vs PyTorch vs TensorFlow
GPU (CNN) deployment size compared: OpenNN CUDA artifact is far smaller than PyTorch and TensorFlow, for edge GPUs like Jetson and single-card workstations.
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CPU HIGGS dense training: OpenNN vs PyTorch vs TensorFlow
With Intel oneMKL on eight i9-12900K P-core threads, OpenNN reaches a 107.7k samples/s training median across three runs: 1.20x PyTorch, 1.06x TensorFlow, and only 0.7% throughput dispersion.
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GPU HIGGS dense training: OpenNN vs PyTorch vs TensorFlow
OpenNN reaches 6.76M samples/s in fp32 and 11.35M samples/s in bf16 on the canonical HIGGS dense training benchmark.
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Iris API lines of code: OpenNN vs PyTorch vs TensorFlow
OpenNN completes the matched Iris classification workflow in 14 logical application instructions, versus 43 for PyTorch and 23 for TensorFlow.