AWS Compute Blog

This post shows you how to accelerate your AI inference workloads by up to 76% using Intel Advanced Matrix Extensions (AMX) – an accelerator that uses specialized hardware and instructions to perform matrix operations directly on processor cores – on Amazon Elastic Compute Cloud (Amazon EC2) 8th generation instances. You’ll learn when CPU-based inference is cost-effective, how to enable AMX with minimal code changes, and which configurations deliver optimal performance for your models.

In this post, you will learn how to configure AWS Lambda Managed Instances by creating a Capacity Provider that defines your compute infrastructure, associating your Lambda function with that provider, and publishing a function version to provision the execution environments. We will conclude with production best practices including scaling strategies, thread safety, and observability for reliable performance.

A resilient auto scaling policy requires metrics that correlate with application utilization, which may not be tied to system resources. Traditionally, auto scaling policies track system resource such as CPU utilization. These metrics are easily available, but they only work when resource consumption correlates with worker capacity. Factors such as high variance in request processing time, mixed instance types, or natural changes in application behavior over time can break this assumption.

This post walks through a fraud detection system built with durable functions. It also highlights the best practices that you can apply to your own production workflows, from approval processes to data pipelines to AI agent orchestration.

AWS Step Functions recently announced new enhancements to local testing capabilities for Step Functions, introducing API-based testing that developers can use to validate workflows before deploying to AWS. As detailed in our Announcement blog post, the TestState API transforms Step Functions development by enabling individual state testing in isolation or as complete workflows. This supports […]

This post is part 3 of the three-part series ‘Enabling high availability of Amazon EC2 instances on AWS Outposts servers’. We provide you with code samples and considerations for implementing custom logic to automate Amazon Elastic Compute Cloud (EC2) relaunch on Outposts servers. This post focuses on guidance for using Outposts servers with third party storage for boot […]

Customers use AWS Lambda to build Serverless applications for a wide variety of use cases, from simple API backends to complex data processing pipelines. Lambda’s flexibility makes it an excellent choice for many workloads, and with support for up to 10,240 MB of memory, you can now tackle compute-intensive tasks that were previously challenging in a Serverless environment. When you configure a Lambda function’s memory size, you allocate RAM and Lambda automatically provides proportional CPU power. When you configure 10,240 MB, your Lambda function has access to up to 6 vCPUs.

This post is co-written with Neel Patel, Abdullahi Olaoye, Kristopher Kersten, Aniket Deshpande from NVIDIA. Today, we’re excited to announce that the NVIDIA Evo-2 NVIDIA NIM microservice are now listed in Amazon SageMaker JumpStart. You can use this launch to deploy accelerated and specialized NIM microservices to build, experiment, and responsibly scale your drug discovery […]

Business applications often coordinate multiple steps that need to run reliably or wait for extended periods, such as customer onboarding, payment processing, or orchestrating large language model inference. These critical processes require completion despite temporary disruptions or system failures. Developers currently spend significant time implementing mechanisms to track progress, handle failures, and manage resources when […]

Stay current with the latest serverless innovations that can transform your applications. In this 31st quarterly recap, discover the most impactful AWS serverless launches, features, and resources from Q4 2025 that you might have missed.