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1. P-EAGLE: Faster LLM inference with Parallel Speculative Decoding in vLLM

   In this post, we explain how P-EAGLE works, how we integrated it into vLLM starting from v0.16.0 (PR#32887), and how to serve it with our pre-trained checkpoints.

2. Improve operational visibility for inference workloads on Amazon Bedrock with new CloudWatch metrics for TTFT and Estimated Quota Consumption

   Today, we’re announcing two new Amazon CloudWatch metrics for Amazon Bedrock, TimeToFirstToken and EstimatedTPMQuotaUsage. In this post, we cover how these work and how to set alarms, establish baselines, and proactively manage capacity using them.

3. Secure AI agents with Policy in Amazon Bedrock AgentCore

   In this post, you will understand how Policy in Amazon Bedrock AgentCore creates a deterministic enforcement layer that operates independently of the agent's own reasoning. You will learn how to turn natural language descriptions of your business rules into Cedar policies, then use those policies to enforce fine-grained, identity-aware controls so that agents only access the tools and data that...

4. Multimodal embeddings at scale: AI data lake for media and entertainment workloads

   This post shows you how to build a scalable multimodal video search system that enables natural language search across large video datasets using Amazon Nova models and Amazon OpenSearch Service. You will learn how to move beyond manual tagging and keyword-based searches to enable semantic search that captures the full richness of video content.

5. Fine-tuning NVIDIA Nemotron Speech ASR on Amazon EC2 for domain adaptation

   In this post, we explore how to fine-tune a leaderboard-topping, NVIDIA Nemotron Speech Automatic Speech Recognition (ASR) model; Parakeet TDT 0.6B V2. Using synthetic speech data to achieve superior transcription results for specialised applications, we'll walk through an end-to-end workflow that combines AWS infrastructure with the following popular open-source frameworks.

6. Operationalizing Agentic AI Part 1: A Stakeholder’s Guide

   The AWS Generative AI Innovation Center has helped 1,000+ customers move AI into production, delivering millions in documented productivity gains. In this post, we share guidance for leaders across the C-suite: CTOs, CISOs, CDOs, and Chief Data Science/AI officers, as well as business owners and compliance leads.

7. Accelerate custom LLM deployment: Fine-tune with Oumi and deploy to Amazon Bedrock

   In this post, we show how to fine-tune a Llama model using Oumi on Amazon EC2 (with the option to create synthetic data using Oumi), store artifacts in Amazon S3, and deploy to Amazon Bedrock using Custom Model Import for managed inference.

8. Run NVIDIA Nemotron 3 Nano as a fully managed serverless model on Amazon Bedrock

   We are excited to announce that NVIDIA’s Nemotron 3 Nano is now available as a fully managed and serverless model in Amazon Bedrock. This follows our earlier announcement at AWS re:Invent supporting NVIDIA Nemotron 2 Nano 9B and NVIDIA Nemotron 2 Nano VL 12B models. This post explores the technical characteristics of the NVIDIA Nemotron 3 Nano model and discusses potential application use cases....

9. Access Anthropic Claude models in India on Amazon Bedrock with Global cross-Region inference

   In this post, you will discover how to use Amazon Bedrock's Global cross-Region Inference for Claude models in India. We will guide you through the capabilities of each Claude model variant and how to get started with a code example to help you start building generative AI applications immediately.

10. Drive organizational growth with Amazon Lex multi-developer CI/CD pipeline

    In this post, we walk through a multi-developer CI/CD pipeline for Amazon Lex that enables isolated development environments, automated testing, and streamlined deployments. We show you how to set up the solution and share real-world results from teams using this approach.