soumith/convnet-benchmarks resource

Health v2 · maintenance only

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2686 stars · 571 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A benchmarking suite and published results comparing publicly available implementations of convolutional neural networks (convnets) across deep learning frameworks like Torch, Caffe, TensorFlow, and Chainer. It measures forward and backward pass times on popular ImageNet models using NVIDIA GPUs.

Use cases

• compare speed of deep learning frameworks for convnets
• benchmark CNN convolution implementations on GPU
• find the fastest library for AlexNet forward and backward passes
• evaluate CuDNN vs native convolution kernel performance
• measure convnet training throughput across frameworks

When to choose

• you need historical benchmark data comparing pre-2017 deep learning frameworks
• you want reference timing methodology for convnet benchmarks
• you are researching performance of older frameworks like Torch, Caffe, or Nervana neon

When to avoid

• you need up-to-date benchmarks of modern frameworks like PyTorch 2.x or JAX
• you want a maintained benchmarking tool for current hardware
• you need benchmarks beyond convolutional networks

Facets

learning-resource · maturity abandoned

benchmarking machine-learning deep-learning deep-learning performance gpu-computing python convnets cnn benchmark-results deep-learning-frameworks nvidia-gpu linux gpu

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markdown · JSON · MCP: product_card(name="soumith/convnet-benchmarks")