Facebook Inc. today updated its popular artificial intelligence software framework PyTorch with support for new features that enable a more seamless AI model deployment to mobile devices.

To deploy PyTorch models on Arm edge devices, you need to optimize the model, prepare the software, and use the right hardware. These steps help you deploy AI applications at the edge.

PyTorch 2.1 is coming Ganti emphasized that IBM’s efforts to accelerate PyTorch for inferencing are not yet ready for production deployment.

At Cruise, he’s implemented techniques like TensorRT acceleration, CUDA graphs, quantization, and speculative decoding, routinely achieving 10x–100x speedups with no drop in model quality.

From data collection, cleaning, and analysis - the amount of work required to prepare data for an machine learning model is very extensive ...

The latest version of Facebook's open source deep learning library PyTorch comes with quantization, named tensors, and Google Cloud TPU support.

Comparing NVIDIA performance numbers (using INT + TensorRT + Custom model) against AWS’s (through PyTorch, open source model & Bfloat16) may not be an apples to apples comparison, but running ...

AWS and Facebook today announced two new open-source projects around PyTorch, the popular open-source machine learning framework. The first of these is TorchServe, a model-serving framework for ...

NVIDIA will be releasing an update to TensorRT-LLM for AI inferencing, which will allow desktops and laptops running RTX GPUs with at least 8GB of VRAM to run the open-source software. This update ...

The future of machine learning is distributed If you are familiar with ML model deployment, you may know about PMML and PFA. PMML and PFA are existing standards for packaging ML models for deployment.