Monat: April 2026

NVIDIA Nemotron-3-Super-120B, Qwen3.5-9B, and Qwen3.5-27B models now available on Amazon SageMaker JumpStart

NVIDIA’s Nemotron-3-Super-120B, Qwen3.5-9B, and Qwen3.5-27B models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models bring specialized capabilities spanning agentic reasoning, multilingual coding, and advanced instruction following, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure. These models address different enterprise AI challenges with specialized capabilities: Nemotron-3-Super-120B is optimized for collaborative agents and high-volume workloads such as IT ticket automation. It employs a hybrid Latent Mixture-of-Experts (LatentMoE) architecture with Mamba-2 and MoE layers, enabling strong agentic, reasoning, and conversational capabilities useful for multi-agent applications like software development and cybersecurity triaging. Qwen 3.5 9B excels in multilingual coding, instruction following, and long-horizon planning, automating software development workflows and executing complex, multi-step office tasks. Its compact design balances efficiency and performance for resource-constrained environments. Qwen 3.5 27B provides deeper contextual understanding, extended reasoning capabilities, and enhanced spatial/complex scenario comprehension, ideal for advanced multimodal reasoning and large-scale document processing. With SageMaker JumpStart, customers can deploy any of these models with just a few clicks to address their specific AI use cases. To get started with these models, navigate to the SageMaker JumpStart model catalog in the SageMaker console or use the SageMaker Python SDK to deploy the models to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Quelle: aws.amazon.com

Amazon EC2 C8gn, M8gn, and R8gn instances now support higher Amazon EBS-optimized performance

Today, AWS announces increased Amazon Elastic Block Store (Amazon EBS) performance for Amazon EC2 C8gn, M8gn, and R8gn instances in 48xlarge and metal-48xl sizes.
EC2 C8gn, M8gn, and R8gn instances are network optimized instances powered by AWS Graviton4 processors and latest 6th generation AWS Nitro Cards. With the latest enhancements to AWS Nitro System, we have doubled the maximum EBS performance on these instances in 48xlarge and metal-48xl sizes, from 60 Gbps of EBS bandwidth and 240,000 IOPS to 120 Gbps of EBS bandwidth and 480,000 IOPS. Customers running network-intensive workloads while requiring additional block storage performance such as data analytics and high-performance file systems can benefit from the improved EBS performance.
All existing and new C8gn, M8gn, and R8gn instances in 48xlarge and metal-48xl sizes launched starting today will benefit from this performance increase at no additional cost. For running instances, customers can stop and start instances to enable this performance increase. The higher EBS performance is available in all AWS regions where these instance types are generally available today.
To learn more, see Amazon C8gn, M8gn, and R8gn Instances and EBS-optimized instance types. 
Quelle: aws.amazon.com

Amazon CloudWatch Logs Insights now supports saved queries with parameters

Amazon CloudWatch Logs Insights saved queries now support parameters, allowing you to pass values to reusable query templates with placeholders. This eliminates the need to maintain multiple copies of nearly identical queries that differ only in specific values such as log levels, service names, or time intervals. You can define up to 20 parameters in a query, with each parameter supporting optional default values. For example, you can create a single template to query logs by severity level (such as ERROR or WARN) and pass different service names each time you run it. To execute a query with parameters, invoke it using the query name prefixed with $ and pass your parameter values, such as $ErrorsByService(logLevel=”ERROR”, serviceName=”OrderEntry”). You can also use multiple saved queries with parameters together for complex log analysis, significantly reducing query maintenance overhead while improving reusability. Saved queries with parameters are available in all commercial AWS regions. You can create and use saved queries with parameters using the Amazon CloudWatch console, AWS Command Line Interface (AWS CLI), AWS Cloud Development Kit (AWS CDK), and AWS SDKs. To learn more, see the Amazon CloudWatch Logs documentation.
Quelle: aws.amazon.com

Aurora DSQL launches connector that simplifies building PHP applications

Today we are announcing the release of the Aurora DSQL Connector for PHP (PDO_PGSQL) that makes it easy to build PHP applications on Aurora DSQL. The PHP Connector streamlines authentication and eliminates security risks associated with traditional user-generated passwords by automatically generating tokens for each connection, ensuring valid tokens are always used while maintaining full compatibility with existing PDO_PGSQL features. The connector handles IAM token generation, SSL configuration, and connection pooling, enabling customers to scale from simple scripts to production workloads without changing their authentication approach. It also provides opt-in optimistic concurrency control (OCC) retry with exponential backoff, custom IAM credential providers, and AWS profile support, making it easier to develop client retry logic and manage AWS credentials. To get started, visit the Connectors for Aurora DSQL documentation page. For code examples, visit our GitHub page for the PHP connector. Get started with Aurora DSQL for free with the AWS Free Tier. To learn more about Aurora DSQL, visit the webpage.    
Quelle: aws.amazon.com

Amazon Redshift introduces key performance optimization for Top-K queries

Amazon Redshift further optimizes the processing of top-k queries (queries with ORDER BY and LIMIT clauses) by intelligently skipping irrelevant data blocks to return results faster, dramatically reducing the amount of data processed. This optimization reorders and efficiently adjusts the data blocks to be read based on the ORDER BY column’s min/max values, maintaining only the K most qualifying rows in memory. When the ORDER BY column is sorted or partially sorted, Amazon Redshift now processes only the minimal data blocks needed rather than scanning entire tables, eliminating unnecessary I/O and compute overhead.
This enhancement particularly benefits top-k queries when the data permanently stores in descending order (ORDER BY … DESC LIMIT K) on large tables where qualifying rows are appended at the end of the data storage. Common examples include:

Finding the k most recent orders from millions or billions of transactions
Retrieving top-k best performing products or k worst performing products (top-k in descending order) from your sales catalog containing hundreds of thousands stock keeping units (SKUs) and millions or billions of sales transactions associated with all product SKUs in your sales catalog
Finding the top-k most recent or top-k oldest (top k in descending order) prompts inferred by a foundational large language model (LLM) out of billions of prompts.

With this new optimization, top-k query performance improves dramatically. This optimization for top-k queries is now available in Amazon Redshift at no additional cost starting with patch release P199 across all AWS regions where Amazon Redshift is available. This optimization automatically applies to eligible queries without requiring any query rewrites or configuration changes.
Quelle: aws.amazon.com