Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P5en instances, powered by the latest NVIDIA H200 Tensor Core GPUs. These instances deliver the highest performance in Amazon EC2 for deep learning and high performance computing (HPC) applications.

You can use Amazon EC2 P5en instances for training and deploying increasingly complex large language models (LLMs) and diffusion models powering the most demanding generative AI applications. You can also use P5en instances to deploy demanding HPC applications at scale in pharmaceutical discovery, seismic analysis, weather forecasting, and financial modeling.

P5en instances feature up to 8 H200 GPUs which have 1.7x GPU memory size and 1.5x GPU memory bandwidth than H100 GPUs featured in P5 instances. P5en instances pair the H200 GPUs with high performance custom 4^th Generation Intel Xeon Scalable processors, enabling Gen5 PCIe between CPU and GPU which provides up to 4x the bandwidth between CPU and GPU and boosts AI training and inference performance. P5en, with up to 3200 Gbps of third generation of EFA using Nitro v5, shows up to 35% improvement in latency compared to P5 that uses the previous generation of EFA and Nitro. This helps improve collective communications performance for distributed training workloads such as deep learning, generative AI, real-time data processing, and high-performance computing (HPC) applications. To address customer needs for large scale at low latency, P5en instances are deployed in Amazon EC2 UltraClusters, and provide market-leading scale-out capabilities for distributed training and tightly coupled HPC workloads.

P5en instances are now available in the US East (Ohio), US West (Oregon), and Asia Pacific (Tokyo) AWS Regions and US East (Atlanta) Local Zone us-east-1-atl-2a in the p5en.48xlarge size.

To learn more about P5en instances, see Amazon EC2 P5en Instances .

With the launch of VPC Resources for Amazon VPC Lattice, you can now access all of your application dependencies through a VPC Lattice service network. You’re able to connect to your application dependencies hosted in different VPCs, accounts, and on-premises using additional protocols, including TLS, HTTP, HTTPS, and now TCP. This new feature expands upon the existing HTTP-based services support, enabling you to share a wider range of resources across your organization.

With VPC Resource support, you can add your TCP resources, such as Amazon RDS databases, custom DNS, or IP endpoints, to a VPC Lattice service network. Now, you can share and connect to all your application dependencies, such as HTTP APIs and TCP databases, across thousands of VPCs, simplifying network management and providing centralized visibility with built-in access controls.

VPC Resources are generally available with VPC Lattice in Africa (Cape Town), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Europe (Frankfurt), Europe (Ireland), Europe (Paris), Europe (Stockholm), Middle East (Bahrain), South America (Sao Paulo), US East (N. Virginia), US East (Ohio), US West (Oregon).

To get started, read the VPC Resources launch blog , architecture blog , and VPC Lattice User Guide . Learn more about VPC Lattice, visit Amazon VPC Lattice Getting Started .
 

Latency-optimized inference for foundation models in Amazon Bedrock now available in public preview, delivering faster response times and improved responsiveness for AI applications. Currently, these new inference options support Anthropic’s Claude 3.5 Haiku model and Meta’s Llama 3.1 405B and 70B models offering reduced latency compared to standard models without compromising accuracy. As verified by Anthropic, with latency-optimized inference in Amazon Bedrock, Claude 3.5 Haiku runs faster on AWS than anywhere else. Additionally, with latency-optimized inference in Bedrock, Llama 3.1  405B and 70B runs faster on AWS than any other major cloud provider.

As more customers move their generative AI applications to production, optimizing the end-user experience becomes crucial, particularly for latency-sensitive applications such as real-time customer service chatbots and interactive coding assistants. Using purpose-built AI chips like AWS Trainium2 and advanced software optimizations in Amazon Bedrock, customers can access more options to optimize their inference for a particular use case. Accessing these capabilities requires no additional setup or model fine-tuning, allowing for immediate enhancement of existing applications with faster response times.

Latency-optimized inference is available for Anthropic’s Claude 3.5 Haiku and Meta’s Llama 3.1 405B and 70B in the US East (Ohio) Region via cross-region inference . To get started, visit the Amazon Bedrock console . For more information about Amazon Bedrock and its capabilities, visit the Amazon Bedrock product page , pricing page , and documentation .

GROW with SAP on AWS is now available for subscription from AWS Marketplace. As a complete offering of solutions, best practices, adoption acceleration services, community and learning, GROW with SAP helps any size organization adopt cloud enterprise resource planning (ERP) with speed, predictability, and continuous innovation. GROW with SAP on AWS can be implemented in months instead of years with traditional on-premises ERP implementations.

By implementing GROW with SAP on AWS, you can simplify everyday work, grow your business, and secure your success. At the core of GROW with SAP is SAP S/4HANA Cloud, a full-featured SaaS ERP suite built on the learnings of SAP’s 50+ years of industry best practices. GROW with SAP allows your organization to gain end- to- end process visibility and control with integrated systems across HR, procurement, sales, finance, supply chain, and manufacturing. It also includes SAP Business AI-powered processes leveraging AWS to provide data-driven insights and recommendations. Customers can also innovate with generative AI using their SAP data through Amazon Bedrock models in the SAP generative AI hub. GROW with SAP on AWS takes advantage of AWS Graviton processors, which offer up to 60% less energy than comparable cloud instances for the same performance.

GROW with SAP on AWS is initially available in the US-East Region.

To subscribe to GROW with SAP on AWS, visit the AWS Marketplace listing . Or, to learn more, visit the GROW with SAP on AWS detail page .

Starting today, Amazon Q Developer can also perform code reviews, automatically providing comments on your code in the IDE, flagging suspicious code patterns, providing patches where available, and even assessing deployment risk so you can get feedback on your code quickly.

Q Developer is a generative AI-powered assistant for designing, building, testing, deploying, and maintaining software. Its agents for software development have a deep understanding of your entire code repos, so they can accelerate many tasks beyond coding. By automating the first round of code reviews and improving review consistency, Q Developer empowers code authors to fix issues faster, streamlining the process for both authors and reviewers. With this new capability, Q Developer can help you get immediate feedback for your code reviews and code fixes where available, so you can increase the speed of iteration and improve the quality of your code.

This capability is available in the integrated development environment (IDE) through a new chat command: /review. You can start automating code reviews via the Visual Studio Code and IntelliJ IDEA Integrated Development Environments (IDEs) with both an Amazon Q Developer Free Tier or Pro Tier subscription. For more details on pricing, see Amazon Q Developer pricing . 

This capability is available in all AWS Regions where Amazon Q Developer is available . To get started with generating documentation, visit Amazon Q Developer or read the news blog .

Amazon Q Developer now helps you accelerate operational investigations across your AWS environment in just a fraction of the time. With a deep understanding of your AWS cloud environment and resources, Amazon Q Developer looks for anomalies in your environment, surfaces related signals for you to explore, identifies potential root-cause hypothesis, and suggests next steps to help you remediate issues faster. 

Amazon Q Developer works alongside you throughout your operational troubleshooting journey from issue detection and triaging, through remediation. You can initiate an investigation by selecting the Investigate action on any Amazon CloudWatch data widget across the AWS Management Console. You can also configure Amazon Q to automatically investigate when a CloudWatch alarm is triggered. When an investigation starts, Amazon Q Developer sifts through various signals about your AWS environment including CloudWatch telemetry, AWS CloudTrail Logs, deployment information, changes to resource configuration, and AWS Health events. 

CloudWatch now provides a dedicated investigation experience where teams can collaborate and add findings, view related signals and anomalies, and review suggestions for potential root cause hypotheses. This new capability also provides remediation suggestions for common operational issues across your AWS environment by surfacing relevant AWS Systems Manager Automation runbooks, AWS re:Post articles, and documentation. It also integrates with your existing operational workflows such as Slack via AWS Chatbot. 

The new operational investigation capability within Amazon Q Developer is available at no additional cost during preview in the US East (N. Virginia) Region. To learn more, see getting started and best practice documentation . 

Today, AWS announces the preview of Amazon Aurora DSQL, a new serverless, distributed SQL database with active-active high availability. Aurora DSQL allows you to build always available applications with virtually unlimited scalability, the highest availability, and zero infrastructure management. It is designed to make scaling and resiliency effortless for your applications, and offers the fastest distributed SQL reads and writes.

Aurora DSQL provides virtually unlimited horizontal scaling with the flexibility to independently scale reads, writes, compute, and storage. It automatically scales to meet any workload demand without database sharding or instance upgrades. Its active-active distributed architecture is designed for 99.99% single-Region and 99.999% multi-Region availability with no single point of failure, and automated failure recovery. This ensures that all reads and writes to any Regional endpoint are strongly consistent and durable. Aurora DSQL is PostgreSQL compatible , offering an easy-to-use developer experience.

Aurora DSQL is now available in preview in the following AWS Regions : US East (N. Virginia), US East (Ohio), and US West (Oregon). 

To learn more about Aurora DSQL features and benefits, check out the Aurora DSQL overview page and documentation . Aurora DSQL is available at no charge during preview. Get started in only a few steps by going to the Aurora DSQL console or using the Aurora DSQL API or AWS CLI.

AWS announces Amazon SageMaker Lakehouse, a unified, open, and secure data lakehouse that simplifies your analytics and artificial intelligence (AI). Amazon SageMaker Lakehouse unifies all your data across Amazon S3 data lakes and Amazon Redshift data warehouses, helping you build powerful analytics and AI/ML applications on a single copy of data.

SageMaker Lakehouse gives you the flexibility to access and query your data in-place with Apache Iceberg open standard. All data in SageMaker Lakehouse can be queried from SageMaker Unified Studio (preview) and engines such as Amazon EMR, AWS Glue, Amazon Redshift or Apache Spark. You can secure your data in the lakehouse by defining fine-grained permissions, which are consistently applied across all analytics and ML tools and engines. With SageMaker Lakehouse, you can use your existing investments. You can seamlessly make data from your Redshift data warehouses available for analytics and AI/ML. In addition, you can now create data lakes by leveraging the analytics optimized Redshift Managed Storage (RMS). Bringing data into lakehouse is easy. You can use zero-ETL to bring data from operational databases, streaming services, and applications, or query in-place data via federated query.

SageMaker Lakehouse is available in US East (N. Virginia), US East (Ohio), Europe (Ireland), US West (Oregon), Canada (Central), Europe (Frankfurt), Europe (Stockholm), Europe (London), Asia Pacific (Sydney), Asia Pacific (Hong Kong), Asia Pacific (Tokyo), Asia Pacific (Singapore), Asia Pacific (Seoul), South America (Sao Paulo).

SageMaker Lakehouse is accessible directly from SageMaker Unified Studio . In addition, you can access SageMaker Lakehouse from AWS Console, AWS Glue APIs and CLIs. To learn more, visit SageMaker Lakehouse and read the launch blog . For pricing information please visit here .

We’re excited to announce Amazon Nova, a new generation of state-of-the-art (SOTA) foundation models (FMs) that deliver frontier intelligence and industry leading price performance. Amazon Nova models available today on Amazon Bedrock are:

• Amazon Nova Micro, a text only model that delivers the lowest latency responses at very low cost.
• Amazon Nova Lite, a very low-cost multimodal model that is lightning fast for processing image, video, and text inputs
• Amazon Nova Pro, a highly capable multimodal model with the best combination of accuracy, speed, and cost for a wide range of tasks.
• Amazon Nova Canvas, a state-of-the-art image generation model.
• Amazon Nova Reel, a state-of-the-art video generation model.

Amazon Nova Micro, Amazon Nova Lite, and Amazon Nova Pro are among the fastest and most cost-effective models in their respective intelligence classes. These models have also been optimized to make them easy to use and effective in RAG and agentic applications. With text and vision fine-tuning on Amazon Bedrock, you can customize Amazon Micro, Lite, and Pro to deliver the optimal intelligence, speed, and cost for your needs. With Amazon Nova Canvas and Amazon Nova Reel, you get access to production-grade visual content, with built-in controls for safe and responsible AI use like watermarking and content moderation. You can see the latest benchmarks and examples of these models on the Amazon Nova product page.

Amazon Nova foundation models are available in Amazon Bedrock in the US East (N. Virginia) region. Amazon Nova Micro, Lite, and Pro models are also available in the US West (Oregon), and US East (Ohio) regions via cross-region inference . Learn more about Amazon Nova at the AWS News Blog , the Amazon Nova product page , or the Amazon Nova user guide . You can get started with Amazon Nova foundation models in Amazon Bedrock from the Amazon Bedrock console .

Amazon SageMaker Lakehouse and Amazon Redshift  now support zero-ETL integrations from applications, automating the extraction and loading of data from eight applications, including Salesforce, SAP, ServiceNow, and Zendesk. As an open, unified, and secure lakehouse for your analytics and AI initiatives, Amazon SageMaker Lakehouse enhances these integrations to streamline your data management processes.

These zero-ETL integrations are fully managed by AWS and minimize the need to build ETL data pipelines. With this new zero-ETL integration, you can efficiently extract and load valuable data from your customer support, relationship management, and ERP applications into your data lake and data warehouse for analysis. Zero-ETL integration reduces users’ operational burden and saves the weeks of engineering effort needed to design, build, and test data pipelines. By selecting a few settings in the no-code interface, you can quickly set up your zero-ETL integration to automatically ingest and continually maintain an up-to-date replica of your data in the data lake and data warehouse. Zero-ETL integrations help you focus on deriving insights from your application data, breaking down data silos in your organization and improving operational efficiency. Now run enhanced analysis on your application data using Apache Spark and Amazon Redshift for analytics or machine learning. Optimize your data ingestion processes and focus instead on analysis and gaining insights. 

Amazon SageMaker Lakehouse and Amazon Redshift support for zero-ETL integrations from eight applications is now generally available in the US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Hong Kong), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), Europe (Ireland), and Europe (Stockholm).

You can create and manage integrations using either the AWS Glue console , the AWS Command Line Interface (AWS CLI), or the AWS Glue APIs. To learn more, visit What is zero-ETL and What is AWS Glue .