Deploy frontier AI models on AWS Trainium with SGLang-Neuron
SGLang-Neuron is now publicly available through an AWS Deep Learning Container. With SGLang-Neuron, you can serve models and generate reinforcement learning rollouts with SGLang on AWS Trainium using the same SGLang interface you already know, all running on native PyTorch.
Amazon has made SGLang-Neuron, an AI model serving framework, publicly available for use with AWS Trainium. SGLang-Neuron allows developers to serve models and generate reinforcement learning rollouts using SGLang on AWS Trainium chips. Trainium is a portfolio of AI chips designed for efficient AI training and inference at scale.
The SGLang-Neuron plugin connects SGLang with AWS Trainium, enabling the use of native PyTorch operations and custom kernels written with the Neuron Kernel Interface (NKI) for performance optimization. SGLang-Neuron supports PD disaggregation, where a prefill server computes the prompt KV cache and transfers it to a decode server for token generation.
Serving models on AWS Trainium with SGLang-Neuron involves starting a Trainium3 Amazon EC2 instance with a specific container, launching the SGLang model server, and sending requests to the model server using curl. SGLang-Neuron allows developers to leverage the familiar SGLang serving workflow on Trainium, enabling faster iteration and development of AI models.
Written by urgent.news from AWS Machine Learning's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
