Category: AWS Inferentia

Rufus, an AI-powered shopping assistant, relies on many components to deliver its customer experience including a foundation LLM (for response generation) and a query planner (QP) model for query classification and retrieval enhancement. This post focuses on how the QP model used draft centric speculative decoding (SD)—also called parallel decoding—with AWS AI chips to meet the demands of Prime Day. By combining parallel decoding with AWS Trainium and Inferentia chips, Rufus achieved two times faster response times, a 50% reduction in inference costs, and seamless scalability during peak traffic.

This post is the first in a series where we will run multiple diffusion transformers on Trainium and Inferentia-powered instances. In this post, we show how you can deploy PixArt-Sigma to Trainium and Inferentia-powered instances.

This post demonstrates how to deploy and serve the Mixtral 8x7B language model on AWS Inferentia2 instances for cost-effective, high-performance inference. We’ll walk through model compilation using Hugging Face Optimum Neuron, which provides a set of tools enabling straightforward model loading, training, and inference, and the Text Generation Inference (TGI) Container, which has the toolkit for deploying and serving LLMs with Hugging Face.

In this post, we outline how to get started with deploying the Qwen 2.5 family of models on an Inferentia instance using Amazon Elastic Compute Cloud (Amazon EC2) and Amazon SageMaker using the Hugging Face Text Generation Inference (TGI) container and the Hugging Face Optimum Neuron library. Qwen2.5 Coder and Math variants are also supported.

At ByteDance, we collaborated with Amazon Web Services (AWS) to deploy multimodal large language models (LLMs) for video understanding using AWS Inferentia2 across multiple AWS Regions around the world. By using sophisticated ML algorithms, the platform efficiently scans billions of videos each day. In this post, we discuss the use of multimodal LLMs for video understanding, the solution architecture, and techniques for performance optimization.

As technology continues to evolve, newer models are emerging, offering higher quality, increased flexibility, and faster image generation capabilities. One such groundbreaking model is Stable Diffusion XL (SDXL), released by StabilityAI, advancing the text-to-image generative AI technology to unprecedented heights. In this post, we demonstrate how to efficiently fine-tune the SDXL model using SageMaker Studio. We show how to then prepare the fine-tuned model to run on AWS Inferentia2 powered Amazon EC2 Inf2 instances, unlocking superior price performance for your inference workloads.

In this post, we walk through the steps to deploy the Meta Llama 3.1-8B model on Inferentia 2 instances using Amazon EKS. This solution combines the exceptional performance and cost-effectiveness of Inferentia 2 chips with the robust and flexible landscape of Amazon EKS. Inferentia 2 chips deliver high throughput and low latency inference, ideal for LLMs.

The use of large language models (LLMs) and generative AI has exploded over the last year. With the release of powerful publicly available foundation models, tools for training, fine tuning and hosting your own LLM have also become democratized. Using vLLM on AWS Trainium and Inferentia makes it possible to host LLMs for high performance […]

This post walks you through Datadog’s new integration with AWS Neuron, which helps you monitor your AWS Trainium and AWS Inferentia instances by providing deep observability into resource utilization, model execution performance, latency, and real-time infrastructure health, enabling you to optimize machine learning (ML) workloads and achieve high-performance at scale.

We’re excited to announce the availability of Meta Llama 3.1 8B and 70B inference support on AWS Trainium and AWS Inferentia instances in Amazon SageMaker JumpStart. Trainium and Inferentia, enabled by the AWS Neuron software development kit (SDK), offer high performance and lower the cost of deploying Meta Llama 3.1 by up to 50%. In this post, we demonstrate how to deploy Meta Llama 3.1 on Trainium and Inferentia instances in SageMaker JumpStart.