The tool runs on native PyTorch via an out-of-tree architecture, separating the core server operations from custom chip optimizations. Under this model, the base framework handles prefix caching and request management while the specialized plugin drives hardware-specific attention, graph compilation, and mixture-of-experts logic. Additionally, the system enables prefill and decode disaggregation by transferring calculated key-value caches between servers using NIXL over Elastic Fabric Adapter. Users can deploy workloads directly onto Trainium3 Amazon Elastic Compute Cloud instances using the dedicated container.
Key points
Developers can now execute frontier model inference and reinforcement learning rollouts u…: Developers can now execute frontier model inference and reinforcement learning rollouts using standard SGLang APIs on Trainium.
The modular out-of-tree plugin design enables hardware enhancements to evolve without alt…: The modular out-of-tree plugin design enables hardware enhancements to evolve without altering the core framework logic.
Model serving is immediately accessible on Trainium3 EC2 instances through the pytorch-in…: Model serving is immediately accessible on Trainium3 EC2 instances through the pytorch-inference-sglang-neuronx container.
Sources checked by our analysis pipeline: 1
Artificial Intelligence
Deploy frontier AI models on AWS Trainium with SGLang-Neuron
Today, Amazon announced that SGLang-Neuron is publicly available and can be accessed through an AWS Deep Learning Container, released by RadixArk in collaboration with Annapurna Labs. With SGLang-Neuron, you can serve models and generate reinforcement learning (RL) rollouts with SGLang on AWS Trainium using the same SGLang interface you use today. Trainium is a portfolio of AWS AI chips that delivers strong economics for AI training and inference at scale. Now, running SGLang on Trainium follows the SGLang workflow you already know: you launch models through SGLang’s server interface and invoke the endpoint using the same generation APIs. Underneath that interface, SGLang-Neuron runs on native PyTorch. With its out-of-tree plugin design, Trainium support and SGLang core develop independently and iterate faster.
How SGLang runs on Trainium
SGLang-Neuron is an out-of-tree plugin that connects SGLang with AWS Trainium. Installing the plugin registers the neuron platform through SGLang’s plugin entry points, and setting SGLANG_PLATFORM=neuron
selects it when the server starts. SGLang core continues to provide the API server, request lifecycle, RadixAttention,…
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