Schlagwort: cloud computing

Amazon ElastiCache now supports Graviton4-based M8g, R8g, and C8gn nodes

Amazon ElastiCache now supports Graviton4-based M8g, R8g, and C8gn node families for Valkey and Memcached. Graviton4-based nodes provide up to 47% higher throughput, up to 43% lower P99 latency, and up to 31% better price-performance for on-demand pricing over Graviton3-based nodes of equivalent sizes on Amazon ElastiCache for Valkey, depending on node family, size, and workload configuration.
Graviton4-based nodes also offer more memory per node compared to equivalent Graviton3-based nodes. As an example, an m8g.8xlarge provides 124.65 GiB versus 103.68 GiB on m7g.8xlarge, up to 20% more memory at the same node size. C8gn nodes offer up to 200 Gbps of network bandwidth, enabling you to scale performance and throughput while optimizing the cost of running network-intensive workloads.
M8g, R8g, and C8gn nodes are available in sizes from large to 16xlarge in over 30 AWS Regions, including the AWS GovCloud (US) Regions and the China Regions. For complete information on pricing and regional availability, please refer to the Amazon ElastiCache pricing page. To get started, create a new cluster or modify an existing cluster using the AWS Management Console, AWS SDK, or AWS CLI. To work with ElastiCache using AI coding agents, see Agent tools for ElastiCache. To learn more, see Supported node types in the Amazon ElastiCache User Guide.
Quelle: aws.amazon.com

AgentCore runtime instances are now generally available

Today, AWS announces runtime instances, a new feature in Amazon Bedrock AgentCore that lets you run agents on your own Amazon EC2 instances without managing infrastructure. AgentCore runtime provides purpose-built infrastructure to deploy and operate AI agents securely at scale. Runtime instances complement the existing microVM-based option in AgentCore runtime and give teams running sustained, resource-intensive, or specialized-hardware agents access to the breadth of EC2 instance types, while AgentCore handles provisioning, patching, scaling, and lifecycle management.
Using the AWS Management Console, CLI, SDKs, or APIs, you create a capacity provider that specifies the EC2 instance types your agents need, including GPU-accelerated, memory-optimized, and compute-optimized families, and attach your agents to it. Runtime instances support long-running agent sessions of up to 14 days, while the default serverless, microVM-based runtime is designed for sessions of up to 8 hours that need fast startup . You can choose the right compute for each agent, or run a mix, without changing how you deploy or invoke your agents.
You can use runtime instances in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Asia Pacific (Mumbai), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), Europe (Frankfurt), and Europe (Ireland). You are charged for the management of the compute provisioned, in addition to your Amazon EC2 costs. To get started, visit AWS News Blog or AgentCore documentation. To learn about pricing, visit AgentCore pricing.
Quelle: aws.amazon.com

Amazon EC2 G7 instances are now available in the AWS Europe (Spain) Region

Amazon Elastic Compute Cloud (Amazon EC2) G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs are now available in Europe (Spain) Region. G7 instances deliver up to 4.6x AI inference performance and up to 2.1 graphics performance compared to G6 instances. G7 instances also deliver faster performance for GPU-accelerated data analytics workloads.
Customers can use G7 instances for deploying AI models for language translation, video and image analysis, and speech recognition. They also accelerate graphics workloads such as creating and rendering real-time, cinematic-quality graphics and game streaming. Additionally, G7 instances support video transcoding, spatial computing, and data analytics workloads such as recommender systems, Retrieval Augmented Generation (RAG) inference, and real-time data pipelines. G7 instances feature up to 8 NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs with 32 GB of memory per GPU and custom Intel Xeon 6 processors. They support up to 192 virtual CPUs (vCPUs) and up to 700 Gbps of Elastic Fabric Adapter (EFA) networking bandwidth. They also support up to 768 GiB of system memory, and up to 7.6 TB of local NVMe SSD storage.
You can start using Amazon EC2 G7 instances today in four AWS Regions: US East (N. Virginia and Ohio), US West (Oregon), and Europe (Spain). You can purchase G7 instances as On-Demand Instances, Spot Instances, or as part of Savings Plans.
To get started, visit the AWS Management Console, AWS Command Line Interface (CLI), and AWS SDKs. To learn more, visit the G7 instance page.
Quelle: aws.amazon.com

AWS Lambda console extends console-to-IDE integration to Kiro and Cursor

AWS Lambda console now extends its console-to-IDE integration to support Kiro and Cursor IDEs. This expansion builds on the existing console-to-IDE transition for Visual Studio Code (VS Code), providing a seamless transition between cloud and local development environments for serverless developers using these popular IDEs.
With the expanded console-to-IDE integration, developers can start from the Lambda console and follow a guided setup to begin local development in Kiro or Cursor while preserving their existing code and configurations. This capability also enables developers to easily convert their applications to an AWS Serverless Application Model (AWS SAM) template using Kiro and Cursor, simplifying their Infrastructure as Code (IaC) practices and CI/CD pipeline integration.
This feature is available in all commercial AWS Regions where Lambda is available, at no additional cost.
To get started, click the “Open in Kiro” or “Open in Cursor” button in the Lambda console’s Code tab or in the Getting Started popup when creating a new function. This will automatically open your function in the selected IDE on your local device. To learn more about this experience, visit the Lambda developer guide.
Quelle: aws.amazon.com

Amazon ECS now supports fractional GPU scheduling with Amazon EC2 G6f instances

Amazon Elastic Container Service (Amazon ECS) now supports fractional GPU scheduling with Amazon EC2 G6f instances, enabling you to run your workloads on GPU partitions as small as one-eighth of an NVIDIA L4 Tensor Core GPU with 3 GB of GPU memory. Fractional GPUs give you the flexibility to right-size your containers for small-model AI inference, model experimentation, graphics rendering, and other workloads that do not require a full GPU, helping reduce infrastructure costs compared to provisioning a full GPU instance.
You can request a fractional GPU by setting GPU=0.125, GPU=0.25, or GPU=0.5 in the container definition of your Amazon ECS task definition. Amazon ECS then places the task on a G6f instance that satisfies the request. Fractional GPU configuration is supported on both Amazon ECS Managed Instances and Amazon ECS on EC2. With ECS Managed Instances, you get a fully managed experience where ECS automatically handles instance provisioning, scaling, patching, and lifecycle management, so you can focus on your GPU workloads rather than infrastructure operations. ECS Managed Instances also include capabilities built specifically for accelerated workloads, such as GPU metrics through Amazon CloudWatch Container Insights, and automatic health monitoring that detects GPU hardware failures and replaces unhealthy instances to minimize workload disruption.
This capability is available in all AWS Regions where Amazon EC2 G6f instances are available. To get started, use the AWS Management Console, AWS CLI, AWS SDKs, AWS CloudFormation, or other infrastructure-as-code tools to configure a capacity provider with G6f instances and specify a fractional GPU value in the container definition of your ECS task definition. To learn more, visit the Amazon ECS fractional GPU documentation and the Amazon EC2 G6 instance page.
Quelle: aws.amazon.com