PyTorch Conference announces native backends for Google TPU and AWS Trainium
According to the PyTorch Blog, PyTorch Conference North America 2026 drew more than 3,500 attendees to San Jose on October 20–21 for discussions on open intelligence, research, models, infrastructure, hardware and production. The event introduced TorchTPU, an open-source Google and Meta collaboration providing a native PyTorch backend for Google TPUs, and launched an Apache 2.0 public beta of TorchNeuron, a native backend for AWS Trainium. Other updates covered compiler development for IBM Spyre and an optional backend for AMD MI350-series GPUs. TorchTitan also expanded model support across its 5D parallelism stack and added reinforcement learning through TitanRL.
The event added native PyTorch backends for Google TPUs and AWS Trainium, giving researchers and practitioners two more hardware options for PyTorch workloads. The IBM Spyre and AMD items were compiler and backend development rather than equivalent native backend releases.
PyTorch Conference North America 2026 brought 3,500+ attendees to San Jose on October 20–21 for two days focused on accelerating open intelligence and exploring the impact of PyTorch Foundation projects like PyTorch, vLLM, DeepSpeed, Ray, Helion, and Safetensors.
PyTorch Foundation Executive Director, Mark Collier, set the stage for the entire event to demonstrate how the PyTorch Foundation is building the open source software to train and serve models on any chip, in any cloud, for any agent.
Accelerating Open Intelligence Together
Mark Collier’s opening keynotes connected work across research, models, frameworks, infrastructure, hardware, and production. Day 1 examined how shared advances across those layers help the community move faster together. Day 2 looked ahead to broader hardware participation, workloads moving across more environments, and training and customizing frontier models at greater scale.
#PyTorch Foundation#PyTorch#PyTorch Conference North America 2026#TorchTPU#Google#Meta#TorchNeuron#AWS Trainium#TorchTitan#TitanRL#IBM Spyre#AMD