AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) Trn3 UltraServers powered by our fourth–generation AI chip Trainium3, our first 3nm AWS AI chip purpose-built to deliver the best token economics for next-generation agentic, reasoning, and video generation applications.

Each AWS Trainium3 chip provides 2.52 petaflops (PFLOPs) of FP8 compute, increases the memory capacity by 1.5x and bandwidth by 1.7x over Trainium2 to 144 GB of HBM3e memory, and 4.9 TB/s of memory bandwidth. Trainium3 is designed for both dense and expert-parallel workloads with advanced data types (MXFP8 and MXFP4) and improved memory-to-compute balance for real-time, multimodal, and reasoning tasks.

Trn3 UltraServers can scale up to 144 Trainium3 chips (362 FP8 PFLOPs total) and are available in EC2 UltraClusters 3.0 to scale to hundreds of thousands of chips. A fully configured Trn3 UltraServer delivers up to 20.7 TB of HBM3e and 706 TB/s of aggregate memory bandwidth. The next-generation Trn3 UltraServer, feature the NeuronSwitch-v1, an all-to-all fabric that doubles interchip interconnect bandwidth over Trn2 UltraServer.

Trn3 delivers up to 4.4x higher performance, 3.9x higher memory bandwidth and 4x better performance/watt compared to our Trn2 UltraServers, providing the best price-performance for training and serving frontier-scale models, including reinforcement learning, Mixture-of-Experts (MoE), reasoning, and long-context architectures. On Amazon Bedrock, Trainium3 is our fastest accelerator, delivering up to 3× faster performance than Trainium2 with over 5× higher output tokens per megawatt at similar latency per user.

New Trn3 UltraServers are built for AI researchers and powered by the AWS Neuron SDK, to unlock breakthrough performance. With native PyTorch integration, developers can train and deploy without changing a single line of model code. For AI performance engineers, we’ve enabled deeper access to Trainium3 so they can fine-tune performance, customize kernels, and push models even further. Because innovation thrives on openness, we are committed to engaging with our developers through open-source tools and resources.

 

​AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) Trn3 UltraServers powered by our fourth–generation AI chip Trainium3, our first 3nm AWS AI chip purpose-built to deliver the best token economics for next-generation agentic, reasoning, and video generation applications. Each AWS Trainium3 chip provides 2.52 petaflops (PFLOPs) of FP8 compute, increases the memory capacity by 1.5x and bandwidth by 1.7x over Trainium2 to 144 GB of HBM3e memory, and 4.9 TB/s of memory bandwidth. Trainium3 is designed for both dense and expert-parallel workloads with advanced data types (MXFP8 and MXFP4) and improved memory-to-compute balance for real-time, multimodal, and reasoning tasks. Trn3 UltraServers can scale up to 144 Trainium3 chips (362 FP8 PFLOPs total) and are available in EC2 UltraClusters 3.0 to scale to hundreds of thousands of chips. A fully configured Trn3 UltraServer delivers up to 20.7 TB of HBM3e and 706 TB/s of aggregate memory bandwidth. The next-generation Trn3 UltraServer, feature the NeuronSwitch-v1, an all-to-all fabric that doubles interchip interconnect bandwidth over Trn2 UltraServer. Trn3 delivers up to 4.4x higher performance, 3.9x higher memory bandwidth and 4x better performance/watt compared to our Trn2 UltraServers, providing the best price-performance for training and serving frontier-scale models, including reinforcement learning, Mixture-of-Experts (MoE), reasoning, and long-context architectures. On Amazon Bedrock, Trainium3 is our fastest accelerator, delivering up to 3× faster performance than Trainium2 with over 5× higher output tokens per megawatt at similar latency per user. New Trn3 UltraServers are built for AI researchers and powered by the AWS Neuron SDK, to unlock breakthrough performance. With native PyTorch integration, developers can train and deploy without changing a single line of model code. For AI performance engineers, we’ve enabled deeper access to Trainium3 so they can fine-tune performance, customize kernels, and push models even further. Because innovation thrives on openness, we are committed to engaging with our developers through open-source tools and resources.  

Amazon S3 increased the maximum object size to 50 TB, a 10x increase from the previous 5 TB limit. This simplifies the processing of large objects such as high-resolution videos, seismic data files, AI training datasets and more. You can store 50 TB objects in all S3 storage classes and use them with all S3 features.

Optimize upload and download performance for your large objects by using the latest AWS Common Runtime (CRT) and S3 Transfer Manager in the AWS SDK. You can apply S3’s storage management capabilities to these objects. For example, use S3 Lifecycle to automatically archive infrequently accessed objects to S3 Glacier storage classes, or use S3 Replication to copy objects across AWS accounts or Regions.

Amazon S3 supports objects up to 50 TB in all AWS Regions. To learn more about working with large objects, visit the S3 User Guide. 

 

​Amazon S3 increased the maximum object size to 50 TB, a 10x increase from the previous 5 TB limit. This simplifies the processing of large objects such as high-resolution videos, seismic data files, AI training datasets and more. You can store 50 TB objects in all S3 storage classes and use them with all S3 features. Optimize upload and download performance for your large objects by using the latest AWS Common Runtime (CRT) and S3 Transfer Manager in the AWS SDK. You can apply S3’s storage management capabilities to these objects. For example, use S3 Lifecycle to automatically archive infrequently accessed objects to S3 Glacier storage classes, or use S3 Replication to copy objects across AWS accounts or Regions. Amazon S3 supports objects up to 50 TB in all AWS Regions. To learn more about working with large objects, visit the S3 User Guide.   

AWS announces Amazon EC2 X8aedz, next generation memory optimized instances, powered by 5th Gen AMD EPYC processors (formerly code named Turin). These instances offer the highest maximum CPU frequency, 5GHz in the cloud. They deliver up to 2x higher compute performance and 31% price-performance compared to previous generation X2iezn instances.

X8aedz instances are built using the latest sixth generation AWS Nitro Cards and are ideal for electronic design automation (EDA) workloads such as physical layout and physical verification jobs, and relational databases that benefit from high single-threaded processor performance and a large memory footprint. The combination of 5 GHz processors and local NVMe storage enables faster processing of memory-intensive backend EDA workloads such as floor planning, logic placement, clock tree synthesis (CTS), routing, and power/signal integrity analysis.

X8aedz instances feature a 32:1 ratio of memory to vCPU and are available in 8 sizes ranging from 2 to 96 vCPUs with 64 to 3,072 GiB of memory, including two bare metal variants, and up to 8 TB of local NVMe SSD storage.

X8aedz instances are now available in US West (Oregon) and Asia Pacific (Tokyo) regions. Customers can purchase X8aedz instances via Savings Plans, On-Demand instances, and Spot instances. To get started, sign in to the AWS Management Console. For more information visit the Amazon EC2 X8aedz instance page or AWS news blog.

 

​AWS announces Amazon EC2 X8aedz, next generation memory optimized instances, powered by 5th Gen AMD EPYC processors (formerly code named Turin). These instances offer the highest maximum CPU frequency, 5GHz in the cloud. They deliver up to 2x higher compute performance and 31% price-performance compared to previous generation X2iezn instances. X8aedz instances are built using the latest sixth generation AWS Nitro Cards and are ideal for electronic design automation (EDA) workloads such as physical layout and physical verification jobs, and relational databases that benefit from high single-threaded processor performance and a large memory footprint. The combination of 5 GHz processors and local NVMe storage enables faster processing of memory-intensive backend EDA workloads such as floor planning, logic placement, clock tree synthesis (CTS), routing, and power/signal integrity analysis. X8aedz instances feature a 32:1 ratio of memory to vCPU and are available in 8 sizes ranging from 2 to 96 vCPUs with 64 to 3,072 GiB of memory, including two bare metal variants, and up to 8 TB of local NVMe SSD storage. X8aedz instances are now available in US West (Oregon) and Asia Pacific (Tokyo) regions. Customers can purchase X8aedz instances via Savings Plans, On-Demand instances, and Spot instances. To get started, sign in to the AWS Management Console. For more information visit the Amazon EC2 X8aedz instance page or AWS news blog.  

Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6e-GB300 UltraServers. P6e-GB300 UltraServers, accelerated by NVIDIA GB300 NVL72, provide 1.5x GPU memory and 1.5x FP4 compute (without sparsity) compared to P6e-GB200. 

Customers can optimize performance for the most powerful models in production with P6e-GB300 for applications that require higher context and implement emerging inference techniques like reasoning and Agentic AI.

To get started with P6e-GB300 UltraServers, please contact your AWS sales representative.

To learn more about P6e UltraServers and instances, visit Amazon EC2 P6 instances.

 

​Today, AWS announces the general availability of Amazon Elastic Compute Cloud (Amazon EC2) P6e-GB300 UltraServers. P6e-GB300 UltraServers, accelerated by NVIDIA GB300 NVL72, provide 1.5x GPU memory and 1.5x FP4 compute (without sparsity) compared to P6e-GB200. 
Customers can optimize performance for the most powerful models in production with P6e-GB300 for applications that require higher context and implement emerging inference techniques like reasoning and Agentic AI.
To get started with P6e-GB300 UltraServers, please contact your AWS sales representative.
To learn more about P6e UltraServers and instances, visit Amazon EC2 P6 instances.  

Amazon Relational Database Service (Amazon RDS) for SQL Server now offers Microsoft SQL Server 2022 Developer Edition. SQL Server Developer Edition is a free edition of SQL Server that contains all the features of Enterprise Edition and can be used in any non-production environment. This enables customers to build, test, and demonstrate applications using SQL Server while reducing costs and maintaining consistency with their production database configurations.

Previously, customers that created Amazon RDS for SQL Server instances for development and test environments had to use SQL Server Standard Edition or SQL Server Enterprise Edition, which resulted in additional database licensing costs for non-production usage. Now, customers can lower the cost of their Amazon RDS development and testing instances by using SQL Server Developer Edition. Furthermore, Amazon RDS for SQL Server features such as automated backups, automated software updates, monitoring, and encryption for development and testing purposes will work on Developer Edition.

The license for Microsoft SQL Server Developer Edition strictly limits its use to development and testing purposes. It cannot be used in a production environment, or for any commercial purposes that directly serve end-users. For more information, refer to the Amazon RDS for SQL Server User Guide. See Amazon RDS for SQL Server Pricing for pricing details and regional availability. 

 

​Amazon Relational Database Service (Amazon RDS) for SQL Server now offers Microsoft SQL Server 2022 Developer Edition. SQL Server Developer Edition is a free edition of SQL Server that contains all the features of Enterprise Edition and can be used in any non-production environment. This enables customers to build, test, and demonstrate applications using SQL Server while reducing costs and maintaining consistency with their production database configurations. Previously, customers that created Amazon RDS for SQL Server instances for development and test environments had to use SQL Server Standard Edition or SQL Server Enterprise Edition, which resulted in additional database licensing costs for non-production usage. Now, customers can lower the cost of their Amazon RDS development and testing instances by using SQL Server Developer Edition. Furthermore, Amazon RDS for SQL Server features such as automated backups, automated software updates, monitoring, and encryption for development and testing purposes will work on Developer Edition. The license for Microsoft SQL Server Developer Edition strictly limits its use to development and testing purposes. It cannot be used in a production environment, or for any commercial purposes that directly serve end-users. For more information, refer to the Amazon RDS for SQL Server User Guide. See Amazon RDS for SQL Server Pricing for pricing details and regional availability.   

You can now attach Amazon S3 Access Points to your Amazon FSx for NetApp ONTAP file systems so that you can access your file data as if it were in S3. With this new capability, your file data in FSx for NetApp ONTAP is effortlessly accessible for use with the broad range of artificial intelligence, machine learning, and analytics services and applications that work with S3 while your file data continues to reside in your FSx for NetApp ONTAP file system.

Amazon FSx for NetApp ONTAP is the first and only complete, fully managed NetApp ONTAP file system in the cloud, allowing you to migrate on-premises applications that rely on NetApp ONTAP or other NAS appliances to AWS without having to change how you manage your data. An S3 Access Point is an endpoint that helps control and simplify how different applications or users can access data. Now, with S3 Access Points for FSx for NetApp ONTAP, you can discover new insights, innovate faster, and make even better data-driven decisions with the data you migrate to AWS. For example, you can use your data to augment generative AI applications with Amazon Bedrock, train machine learning models with Amazon SageMaker, run analysis using Amazon Glue or a wide range of AWS Data and Analytics Competency Partner solutions, and run workflows using S3-based cloud-native applications.

Get started with this capability by creating and attaching S3 Access Points to new FSx for NetApp ONTAP file systems using the Amazon FSx console, the AWS Command Line Interface (AWS CLI), or the AWS Software Development Kit (AWS SDK). Support for existing FSx for NetApp ONTAP file systems will come in an upcoming weekly maintenance window. This new capability is available in the select AWS Regions.

To get started, see the following list of resources:

• Amazon FSx for NetApp ONTAP
• Amazon S3 Access Points
• AWS News Blog

 

​You can now attach Amazon S3 Access Points to your Amazon FSx for NetApp ONTAP file systems so that you can access your file data as if it were in S3. With this new capability, your file data in FSx for NetApp ONTAP is effortlessly accessible for use with the broad range of artificial intelligence, machine learning, and analytics services and applications that work with S3 while your file data continues to reside in your FSx for NetApp ONTAP file system. Amazon FSx for NetApp ONTAP is the first and only complete, fully managed NetApp ONTAP file system in the cloud, allowing you to migrate on-premises applications that rely on NetApp ONTAP or other NAS appliances to AWS without having to change how you manage your data. An S3 Access Point is an endpoint that helps control and simplify how different applications or users can access data. Now, with S3 Access Points for FSx for NetApp ONTAP, you can discover new insights, innovate faster, and make even better data-driven decisions with the data you migrate to AWS. For example, you can use your data to augment generative AI applications with Amazon Bedrock, train machine learning models with Amazon SageMaker, run analysis using Amazon Glue or a wide range of AWS Data and Analytics Competency Partner solutions, and run workflows using S3-based cloud-native applications. Get started with this capability by creating and attaching S3 Access Points to new FSx for NetApp ONTAP file systems using the Amazon FSx console, the AWS Command Line Interface (AWS CLI), or the AWS Software Development Kit (AWS SDK). Support for existing FSx for NetApp ONTAP file systems will come in an upcoming weekly maintenance window. This new capability is available in the select AWS Regions. To get started, see the following list of resources:

Amazon FSx for NetApp ONTAP
Amazon S3 Access Points
AWS News Blog  

Amazon RDS for SQL Server launches optimize CPU with support for M7i and R7i instance families, which reduce prices by up to 55% compared to equivalent previous generation instances. Optimize CPU optimizes Simultaneous Multi-threading (SMT) configuration to reduce commercial software charges. Customers can lower cost by upgrading to M7i and R7i instances from similar 6th generation instances. Furthermore, for memory or IO intensive database workloads, customers can get additional cost reduction by fine tuning optimize CPU configuration.

RDS for SQL Server price for database instance hours consumed is inclusive of Microsoft Windows and Microsoft SQL Server software charges. Optimize CPU disables SMT for instances with 2 or more physical CPU cores. This reduces the number of vCPUs, and the corresponding commercial software charges by 50% while providing the same number of physical CPU cores, and near equivalent performance. The most significant savings are available on 2Xlarge and higher instances, and instances that use Multi-AZ deployment, where RDS optimizes to reduce SQL Server software charges for only a single active node for most usage. For workloads that are memory or IO intensive, customers can fine tune the number of active physical CPU cores for further savings.

RDS for SQL Server supports M7i and R7i instances in all AWS Regions. With unbundled instance pricing, database costs are calculated with separate charges for third party licensing fees per vCPU hour, and third party licensing fees are not eligible towards your organization’s discounts with AWS. You can view Microsoft Windows and SQL Server charges associated with your usage on AWS Billing and Cost Management, and in monthly bills. For more details, visit RDS for SQL Server pricing, Amazon RDS User Guide and AWS News Blog.

 

​Amazon RDS for SQL Server launches optimize CPU with support for M7i and R7i instance families, which reduce prices by up to 55% compared to equivalent previous generation instances. Optimize CPU optimizes Simultaneous Multi-threading (SMT) configuration to reduce commercial software charges. Customers can lower cost by upgrading to M7i and R7i instances from similar 6th generation instances. Furthermore, for memory or IO intensive database workloads, customers can get additional cost reduction by fine tuning optimize CPU configuration. RDS for SQL Server price for database instance hours consumed is inclusive of Microsoft Windows and Microsoft SQL Server software charges. Optimize CPU disables SMT for instances with 2 or more physical CPU cores. This reduces the number of vCPUs, and the corresponding commercial software charges by 50% while providing the same number of physical CPU cores, and near equivalent performance. The most significant savings are available on 2Xlarge and higher instances, and instances that use Multi-AZ deployment, where RDS optimizes to reduce SQL Server software charges for only a single active node for most usage. For workloads that are memory or IO intensive, customers can fine tune the number of active physical CPU cores for further savings. RDS for SQL Server supports M7i and R7i instances in all AWS Regions. With unbundled instance pricing, database costs are calculated with separate charges for third party licensing fees per vCPU hour, and third party licensing fees are not eligible towards your organization’s discounts with AWS. You can view Microsoft Windows and SQL Server charges associated with your usage on AWS Billing and Cost Management, and in monthly bills. For more details, visit RDS for SQL Server pricing, Amazon RDS User Guide and AWS News Blog.  

AWS Transform adds powerful new agentic AI capabilities to automate VMware migrations to AWS. The migration agent collaborates with migration teams to understand business priorities and intelligently plan and migrate hundreds of applications spanning thousands of servers, significantly reducing manual effort, time, and complexity.

The agent can now discover your on-premises environment and prioritize applications for migration using the AWS Transform discovery tool, inventory data from various third-party discovery tools, and unstructured data such as documents, notes, and business rules. It analyzes infrastructure, database, and application details, maps dependencies, and generates migration plans grouped by business and technical priorities such as ownership, department, function, subnet, and operating systems. It generates networks with hub-and-spoke and isolated network configurations, provides flexible IP address management options, deploys to multiple accounts, generates network configurations for your AWS landing zones, and migrates from source environments like NSX, Palo Alto, Fortigate, and Cisco ACI. The agent migrates servers to AWS securely and iteratively in waves and provides clear progress updates throughout the deployment. It also migrates Windows and Linux x86 servers, hypervisors such as VMware, HyperV, Nutanix, and KVM, and bare-metal physical environments to multiple target accounts. Throughout your migration, you can ask the agent questions as it guides your decisions, whether that’s repeating or skipping steps, or adjusting plans. To simplify internal approvals, the agent also generates a detailed report with the migration plan and mapping of networks, servers, and applications.

With AWS Transform, you can accelerate time to value, lower risk, and reduce the complexity of VMware migrations. These new capabilities are available in all AWS Regions where AWS Transform is offered, with support for migrating servers and networks to 16 AWS Regions.

Learn more on the product page and user guide, and get started with AWS Transform.

 

​AWS Transform adds powerful new agentic AI capabilities to automate VMware migrations to AWS. The migration agent collaborates with migration teams to understand business priorities and intelligently plan and migrate hundreds of applications spanning thousands of servers, significantly reducing manual effort, time, and complexity. The agent can now discover your on-premises environment and prioritize applications for migration using the AWS Transform discovery tool, inventory data from various third-party discovery tools, and unstructured data such as documents, notes, and business rules. It analyzes infrastructure, database, and application details, maps dependencies, and generates migration plans grouped by business and technical priorities such as ownership, department, function, subnet, and operating systems. It generates networks with hub-and-spoke and isolated network configurations, provides flexible IP address management options, deploys to multiple accounts, generates network configurations for your AWS landing zones, and migrates from source environments like NSX, Palo Alto, Fortigate, and Cisco ACI. The agent migrates servers to AWS securely and iteratively in waves and provides clear progress updates throughout the deployment. It also migrates Windows and Linux x86 servers, hypervisors such as VMware, HyperV, Nutanix, and KVM, and bare-metal physical environments to multiple target accounts. Throughout your migration, you can ask the agent questions as it guides your decisions, whether that’s repeating or skipping steps, or adjusting plans. To simplify internal approvals, the agent also generates a detailed report with the migration plan and mapping of networks, servers, and applications. With AWS Transform, you can accelerate time to value, lower risk, and reduce the complexity of VMware migrations. These new capabilities are available in all AWS Regions where AWS Transform is offered, with support for migrating servers and networks to 16 AWS Regions. Learn more on the product page and user guide, and get started with AWS Transform.  

AWS Transform for mainframe delivers new data and activity analysis capabilities to extract comprehensive insights to drive the reimagining of mainframe applications. These insights can be combined with business logic extraction to inform decomposition of legacy applications into logical business domains. Together, these form the basis of a comprehensive specification for coding agents like Kiro to reimagine applications into cloud-native architectures.

The new capabilities empower organizations to reimagine legacy workloads, providing a comprehensive reverse engineering workflow that includes automated code and data structure analysis, activity analysis, technical documentation generation, business logic extraction, and intelligent code decomposition. Through in-depth data and activity analysis, AWS Transform helps identify application components with high utilization or business value, allowing teams to optimize their modernization efforts and make data-informed architectural decisions.

In the AI-powered chat interface, users can customize their modernization approach through flexible job plans that allow them to select predefined comprehensive workflows—full modernization, analysis focus, or business logic focus—or create their own combination of capabilities based on specific objectives.

The reimagine capabilities in AWS Transform for mainframe are available today in US East (N. Virginia), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London) Regions.

To learn more about reimagining mainframe applications with AWS Transform for mainframe, read the AWS News Blog post or visit the AWS Transform product page. 

 

​AWS Transform for mainframe delivers new data and activity analysis capabilities to extract comprehensive insights to drive the reimagining of mainframe applications. These insights can be combined with business logic extraction to inform decomposition of legacy applications into logical business domains. Together, these form the basis of a comprehensive specification for coding agents like Kiro to reimagine applications into cloud-native architectures. The new capabilities empower organizations to reimagine legacy workloads, providing a comprehensive reverse engineering workflow that includes automated code and data structure analysis, activity analysis, technical documentation generation, business logic extraction, and intelligent code decomposition. Through in-depth data and activity analysis, AWS Transform helps identify application components with high utilization or business value, allowing teams to optimize their modernization efforts and make data-informed architectural decisions. In the AI-powered chat interface, users can customize their modernization approach through flexible job plans that allow them to select predefined comprehensive workflows—full modernization, analysis focus, or business logic focus—or create their own combination of capabilities based on specific objectives. The reimagine capabilities in AWS Transform for mainframe are available today in US East (N. Virginia), Asia Pacific (Mumbai), Asia Pacific (Seoul), Asia Pacific (Sydney), Asia Pacific (Tokyo), Canada (Central), Europe (Frankfurt), and Europe (London) Regions. To learn more about reimagining mainframe applications with AWS Transform for mainframe, read the AWS News Blog post or visit the AWS Transform product page.   

Amazon Connect now supports creation of custom metrics, enabling contact center supervisors to analyze tailored performance measurements without requiring technical skills. This feature provides a simple, no-code interface for performing mathematical operations (e.g., addition, subtraction, sum, average) on existing Connect data to build metrics that align with your organization’s specific business requirements. Custom metrics are available to use in the dashboards and APIs.

With custom metrics, you can track performance in ways that matter most to your business. For example, create average handle time metrics for premium versus standard customer segments, calculate total agent time on outbound calls by product line, or measure queue performance filtered by contact type such as callbacks versus incoming calls.
This new feature is available in all AWS regions where Amazon Connect is offered. To learn more about Amazon Connect custom metrics, see the Administrator Guide. To learn more about Amazon Connect, see the Amazon Connect website.

 

​Amazon Connect now supports creation of custom metrics, enabling contact center supervisors to analyze tailored performance measurements without requiring technical skills. This feature provides a simple, no-code interface for performing mathematical operations (e.g., addition, subtraction, sum, average) on existing Connect data to build metrics that align with your organization’s specific business requirements. Custom metrics are available to use in the dashboards and APIs. With custom metrics, you can track performance in ways that matter most to your business. For example, create average handle time metrics for premium versus standard customer segments, calculate total agent time on outbound calls by product line, or measure queue performance filtered by contact type such as callbacks versus incoming calls. This new feature is available in all AWS regions where Amazon Connect is offered. To learn more about Amazon Connect custom metrics, see the Administrator Guide. To learn more about Amazon Connect, see the Amazon Connect website.