ExecuTorch 1.0 allows developers to deploy PyTorch models directly to edge devices, including iOS and Android devices, PCs, and embedded systems, with CPU, GPU, and NPU hardware acceleration.

Google has launched TorchTPU, an engineering stack enabling PyTorch workloads to run natively on TPU infrastructure for enterprise AI.

What if the key to unlocking faster, more efficient machine learning workflows lies not in your algorithms but in the hardware powering them? In the world of GPUs, where raw computational power meets ...

Nvidia has updated its CUDA software platform, adding a programming model designed to simplify GPU management. Added in what the chip giant claims is its “biggest evolution” since its debut back in ...

Graphics processing units have fundamentally reshaped how professionals across numerous disciplines approach demanding ...

The PyTorch Foundation also welcomed Safetensors as a PyTorch Foundation-hosted project. Developed and maintained by Hugging ...

New platform closes the AI Deployment Gap by delivering certifiable AI execution across heterogeneous CPU and GPU architectures, reducing certification complexity and risk ...