Deploy frontier AI models on AWS Trainium with SGLang-Neuron

According to AWS Machine Learning:

Background

Amazon announced that SGLang-Neuron is now publicly available through an AWS Deep Learning Container. Built by RadixArk in collaboration with Annapurna Labs, the tool brings SGLang's popular serving framework to AWS Trainium, Amazon's proprietary AI chip portfolio designed for cost-effective training and inference at scale.

Technical Details

SGLang-Neuron operates as an out-of-tree plugin that connects SGLang with AWS Trainium. The core SGLang framework handles the API server, request management, and RadixAttention, while the plugin manages Trainium-specific models, attention mechanisms, Mixture of Experts (MoE), and graph execution using native PyTorch and the Neuron Kernel Interface (NKI). It also supports PD disaggregation, allowing prefill and decode phases to run on separate servers and transfer KV caches via Elastic Fabric Adapter (EFA).

Practical Impact

Developers can now deploy models on Trainium using the exact same SGLang APIs and server interfaces they already use. This lowers the barrier to adopting AWS hardware for large-scale model serving and reinforcement learning workloads without requiring a complete rewrite of their serving pipelines.