Deploy frontier AI models on AWS Trainium with SGLang-Neuron

This article announces the public availability of SGLang-Neuron, an out-of-tree plugin that enables serving AI models on AWS Trainium using SGLang's familiar interface.

• SGLang-Neuron connects SGLang with AWS Trainium through a plugin architecture that allows independent development and faster iteration
• Uses native PyTorch operations and Neuron Kernel Interface (NKI) kernels compiled with torch.compile for optimized execution
• Supports PD disaggregation with prefill and decode servers communicating via NIXL over Elastic Fabric Adapter (EFA)
• Available through AWS Deep Learning Container released by RadixArk in collaboration with Annapurna Labs
• Provides step-by-step walkthrough for launching models on Trainium3 EC2 instances using the pytorch-inference-sglang-neuron container

SGLang-Neuron brings familiar SGLang serving workflows to AWS Trainium, enabling efficient AI model deployment at scale.