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Seekable OCI (SOCI) indexing for Amazon SageMaker AI training and processing jobs takes full container image download off the critical path. SageMaker AI lazy-loads your image to start containers in seconds instead of minutes, with no changes to your job definition. See the benchmarks and how to adopt it.
This article is the first in a series of technical blog posts jointly published by Hugging Face and Amazon, systematically introducing how to use TorchNeuron on AWS Trainium AI accelerator chips for PyTorch model inference performance analysis. It covers the principles of Neuron backend integration, methods for configuring torch.profiler, the use of NeuronConfig and NeuronProfiler tools, and visualized tracing with Neuron Explorer. Additionally, using RMSNorm as an example, it compares the device‑time differences among three implementations: plain PyTorch, the built-in torch.rms_norm fusion, and torch.compile’s automatic fusion.
i14 has launched the Laplace 3B model with 3.028 billion parameters, which replaces Softmax attention with Gated Linear Attention (GLA). During inference, its per-sequence recursive state remains constant at 29.5 MB, independent of context length. The model was trained and validated on a single AWS trn2.48xlarge instance (equipped with 16 Trainium 2 chips), achieving a training throughput of 120,000 tokens per second and an inference throughput of 11,544 output tokens per second (with 1,280 concurrent sequences). All performance metrics are based on internal measurements conducted by i14 and the AWS GenAIIC team.
Hugging Face has released TorchTPU, providing native TPU backend support for PyTorch, enabling libraries such as Transformers and Diffusers to run directly on TPUs without relying on torch_xla or migrating to JAX.
Hugging Face, in collaboration with AWS, has launched TorchNeuron—a PyTorch-native Trainium backend—available for public beta starting October 20, 2026. This technology enables mainstream libraries such as Transformers and Diffusers to run directly on Trainium chips without any modifications, significantly enhancing the usability and compatibility of text-generation and image-generation models.
