Deep Learning Deployment Toolkit | Intel

pip install openvino Assume you have an ONNX export of your PyTorch model:

Stop wrestling with framework dependencies. Start deploying optimized models at the edge. If you have ever trained a beautiful model in PyTorch or TensorFlow only to watch it crawl across the finish line on a production CPU, you know the pain. We’ve all been there: high latency, bloated memory usage, and the sinking feeling that you need to buy expensive GPUs just to serve inference. intel deep learning deployment toolkit

Ditch the Complexity: Supercharge Inference with the Intel Deep Learning Deployment Toolkit pip install openvino Assume you have an ONNX

What if I told you that your existing Intel Xeon CPUs (or even your Core i5 laptop) are hiding a massive amount of untapped performance? The secret isn't buying new hardware; it's using the . We’ve all been there: high latency, bloated memory

The toolkit solves one simple problem:

If you are deploying to CPUs (and let's be honest, 90% of inference still happens on CPUs), you are leaving performance on the table by not using DLDT.

The easiest way to get the runtime is via pip, though for the full Model Optimizer, download the full OpenVINO toolkit.