Tag: Internet

We are pleased to announce general availability of Amazon EC2 P6-B200 instances in AWS US East (N. Virginia) on SageMaker Studio notebooks. Amazon EC2 P6-B200 instances are powered by 8 NVIDIA Blackwell GPUs with 1440 GB of high-bandwidth GPU memory and 5th Generation Intel Xeon processors (Emerald Rapids). These instances deliver up to 2x better performance compared to P5en instances for AI training. Customers can use P6-B200 instances to interactively develop and fine-tune large foundation models, including LLMs, mixture of experts models, and multi-modal reasoning models. These instances enable efficient experimentation with larger models directly in JupyterLab or CodeEditor environments for generative AI applications such as enterprise copilots and content generation across text, images, and video. Visit developer guides for instructions on setting up and using 

Elastic Network Adapter (ENA) Express now supports traffic between Amazon EC2 instances in different Availability Zones within a Region, delivering up to 25 Gbps single-flow bandwidth. ENA Express is a networking feature that uses the AWS Scalable Reliable Datagram (SRD) protocol to improve network performance. SRD is a reliable network protocol that delivers performance improvements through advanced congestion control and multi-pathing. Amazon Elastic Block Store (EBS) io2 Block Express and Elastic Fabric Adapter (EFA) for high performance computing and machine learning workloads also leverage SRD. Workloads such as distributed storage, databases, and file systems require deployments spanning multiple Availability Zones for resilience, yet single flows between zones support up to 5 Gbps with ENA. ENA Express delivers up to 25 Gbps single-flow bandwidth for traffic between Availability Zones. To achieve this, ENA Express detects compatibility between your EC2 instances

We are pleased to announce general availability of Amazon EC2 G6 instances in the Middle East (Dubai) and Asia Pacific (Malaysia) on SageMaker Studio notebooks. Amazon EC2 G6 instances are powered by up to 8 NVIDIA L4 Tensor Core GPUs with 24 GB of memory per GPU and third generation AMD EPYC processors. G6 instances offer 2x better performance for deep learning inference compared to EC2 G4dn instances. Customers can use G6 instances to interactively test model deployment and for interactive model training for use cases such as generative AI fine-tuning and inference workloads, natural language processing, language translation, computer vision, and recommender engines. Visit developer guides for instructions on setting up and using JupyterLab and 

We are pleased to announce general availability of Amazon EC2 P4de instances in Asia Pacific (Tokyo, Singapore) and Europe (Frankfurt) on SageMaker Studio notebooks. Amazon EC2 P4de instances are powered by 8 NVIDIA A100 GPUs with 80GB high-performance HBM2e GPU memory, 2X higher than the GPUs in our current P4d instances. The new P4de instances provide a total of 640GB of GPU memory, which provide up to 60% better ML training performance along with 20% lower cost to train when compared to P4d instances. The improved performance will allow customers to reduce model training times and accelerate time to market. Increased GPU memory on P4de will also benefit workloads that need to train on large datasets of high-resolution data. Visit developer guides for instructions on setting up and using JupyterLab and&nbs

We are pleased to announce general availability of Amazon EC2 G6e instances in the Middle East (Dubai), Asia Pacific (Tokyo, Seoul) and Europe (Frankfurt, Stockholm, Spain) on SageMaker Studio notebooks. Amazon EC2 G6e instances are powered by up to 8 NVIDIA L40s Tensor Core GPUs with 48 GB of memory per GPU and third generation AMD EPYC processors. G6e instances deliver up to 2.5x better performance compared to EC2 G5 instances. Customers can use G6e instances to interactively test model deployment and for interactive model training use cases such as generative AI fine-tuning. You can use G6e instances to deploy large language models (LLMs) with up to 13B parameters and diffusion models for generating images, video, and audio. Visit developer guides for instructions on setting up and using JupyterLab and&nbs

Amazon Aurora DSQL single-Region clusters are now available in Asia Pacific (Hong Kong), Asia Pacific (Mumbai), Asia Pacific (Singapore), Europe (Stockholm), and South America (Sao Paulo). Aurora DSQL is the fastest serverless, distributed SQL database that enables you to build always available applications with virtually unlimited scalability, the highest availability, and zero infrastructure management. It is designed to make scaling and resilience effortless for your applications and offers the fastest distributed SQL reads and writes. With this launch, Aurora DSQL is available in the following AWS Regions: US East (N. Virginia), US East (Ohio), US West (Oregon), Canada (Central), Canada West (Calgary), Asia Pacific (Hong Kong), Asia Pacific (Melbourne), Asia Pacific (Mumbai), Asia Pacific (Osaka), Asia Pacific (Singapore), Asia Pacific (Sydney), , Asia Pacific (Tokyo), Asia P

AWS HealthOmics now supports caching completed task outputs of cancelled runs, enabling customers to reuse outputs and avoid recomputing previously completed tasks. When caching is enabled and a run is cancelled, HealthOmics automatically stores completed task outputs in the customer’s S3 bucket, allowing customers to restart runs from the point of cancellation. AWS HealthOmics is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs at scale with fully managed bioinformatics workflows. Caching of cancelled runs helps researchers, bioinformaticians, and workflow developers debug and iteratively develop workflows efficiently by storing intermediate files and completed task outputs for inspection. This saves customers the cost of recomputing completed tasks that may have taken hours and accelerates subsequent runs by executing only the remaining incomplete tasks.

AWS WAF now supports dynamic label interpolation, enabling you to forward WAF classification signals to your origin and embed context in responses with a single rule. Security engineers who previously maintained a separate rule for every signal value can now use ${namespace:} syntax in custom request headers, response headers, and response bodies to forward an entire label namespace at once. For example, one rule with a dynamic variable can forward all IP reputation signals to your application, which can then respond adaptively, such as by enforcing multi-factor authentication (MFA). Interpolation also introduces synthetic labels: built-in values resolved from request context, including client IP address, WAF request ID, and JA3 and JA4 fingerprints. You can embed these in custom block pages and challenge pages so users reporting false positives have a reference ID to cite, or forward TLS fingerprints to your application for adaptive auth decisions. Interpolation works with

Today, AWS announced the general availability of Claude Platform on AWS, a new service that gives customers direct access to Anthropic's native Claude Platform experience through their existing AWS account. AWS is the first cloud provider to offer access to the native Claude Platform experience. Developers and organizations now have the choice to access Anthropic's native Claude Platform experience, including APIs, console, and early-access beta features, directly through their existing AWS account, without managing separate accounts, billing, or tracking. Claude Platform on AWS is operated by Anthropic, and customer data is processed outside the AWS security boundary. Claude Platform on AWS is designed for development teams and enterprises that want access to Anthropic's native Claude Platform development experience and do not have specific regional data residency requirements. Customers still use existing IAM

AWS Transform now supports replatforming applications to containers during migration to AWS. This release extends AWS Transform's agentic AI capabilities to automate the containerization of your source code, enabling you to migrate and modernize in parallel, reducing the time and complexity of moving from on-premises to cloud-native architectures. Migration teams can containerize source code from GitHub, Bitbucket, GitLab, or .zip files, generate Docker images, publish to Amazon Elastic Container Registry (Amazon ECR), and deploy to Amazon Elastic Container Service (Amazon ECS) or Amazon Elastic Kubernetes Service (Amazon EKS). This brings containerization into the same workflow your team uses to plan and execute rehost migrations. AWS Transform analyzes your source code repositories, generates Dockerfiles, and builds container images with integrated