Amazon Makes SGLang-Neuron Publicly Available: The Same SGLang Interface Now Serves Models on Trainium Chips
Amazon announced on November 12, 2026, on its machine learning blog 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, developers can serve models and generate reinforcement learning rollouts with SGLang on AWS Trainium using the same SGLang interface they use today.
The post said SGLang-Neuron is an out-of-tree plugin that connects SGLang with AWS Trainium and runs on native PyTorch. TorchNeuron registers Trainium as a native PyTorch device, the model execution path uses standard PyTorch operations together with kernels written with the Neuron Kernel Interface, and the plugin also supports prefill and decode disaggregation.
The walkthrough uses a Trainium3 Amazon EC2 instance and a pytorch-inference-sglang-neuronx container to load the openai/gpt-oss-120b model. The example settings include tensor parallel size 8 and expert parallel size 8, an fp8_e4m3 key-value cache data type, up to 64 running requests and a maximum of 655,360 total tokens, followed by a request to a local chat completions endpoint. The post presents these as example deployment settings, directs developers to the project documentation for supported models, features and validated configurations, and reports no throughput, latency or price comparisons.
Source: Amazon
Published by GSM · USA (original Chinese edition)
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