Intel had Q2 revenue of $16.1 billion, up 25% YoY, for a net  loss of $11 billion compared to the $2.9 billion loss in Q2 2025. Gross margin was 40.4% […]

The post Intel makes Q2 loss of $11bn appeared first on Electronics Weekly .

AWS HealthOmics private workflows are now available in the Asia Pacific (Tokyo) and US East (Ohio) Regions, expanding access to fully-managed bioinformatics workflows for research, drug discovery, and agriculture science initiatives with regional compliance requirements. AWS HealthOmics  is a HIPAA-eligible service that helps healthcare and life sciences customers accelerate scientific breakthroughs with fully managed bioinformatics workflows.

With HealthOmics private workflows, customers can build and scale genomics data analysis pipelines using familiar domain-specific languages including Nextflow, WDL, and CWL, enabling healthcare and life sciences customers to focus on scientific discovery rather than infrastructure management. HealthOmics provides built-in features, such as Git integrations for version-controlled workflow development and third-party container registry support through Amazon ECR, to make it easy to migrate existing pipelines and accelerate development of new genomics workflows while maintaining full data provenance and compliance requirements.  

Private workflows are now available in the following AWS Regions: US East (N. Virginia, Ohio), US West (Oregon), Europe (Frankfurt, Ireland, London), Israel (Tel Aviv), and Asia Pacific (Seoul, Singapore, Tokyo). To learn more, visit the AWS HealthOmics User Guide . For more information on pricing, visit AWS HealthOmics pricing .

Amazon SageMaker AI inference now supports G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs, enabling you to deploy machine learning models with up to 4.6x AI inference performance compared to previous-generation G6 instances. Customers deploying generative AI models for production inference need high GPU throughput and memory capacity to serve medium-to-large models cost-effectively, but previous-generation instances often required over-provisioning expensive compute or quantizing models to fit within memory constraints.

G7 instances provide 32 GB of GPU memory per GPU with 5th Generation Tensor Cores, up to 700 Gbps of EFA-enabled networking (7x compared to G6), and up to 7.6 TB of local NVMe SSD storage for keeping large models close to compute. These capabilities make G7 instances well suited for serving models in the 7B–30B parameter range, image and video generation workloads, and multi-model inference endpoints that benefit from higher memory bandwidth and throughput. You can deploy models on G7 instances using the SageMaker AI Inference console, API, or SDK by specifying G7 instance types (such as ml.g7.xlarge through ml.g7.48xlarge) in your endpoint configuration.

G7 instances for SageMaker AI inference are available in US East (N. Virginia, Ohio) and US West (Oregon). For pricing information on these instances, please visit our pricing page .

Amazon EC2 M8id instances are now available in (Ireland). These instances are powered by custom Intel Xeon 6 processors and deliver up to 43% higher performance and 3.3x more memory bandwidth compared to previous generation M6id instances.

M8id instances offer up to 384 vCPUs, 1.5TiB of memory, and 22.8TB of NVMe SSD storage, 3x more than previous generation instances. These instances deliver up to 46% higher performance for I/O intensive database workloads, and up to 30% faster query results for I/O intensive real-time data analytics than previous sixth-generation instances. Additionally, these instances support Instance Bandwidth Configuration, allowing 25% flexible allocation between network and EBS bandwidth, allocating resources optimally for each workload.

M8id instances are well-suited for balanced workloads including application servers, microservices, enterprise applications, and small to medium databases.

Customers can purchase these instances via Savings Plans, On-Demand instances, and Spot instances. For more information visit the Amazon EC2 instance type page.

Amazon EC2 High Memory U7in-16TB instances (u7in-16tb.224xlarge) are now available in AWS South America (São Paulo) region, and U7in-24TB instances (u7in-24tb.224xlarge) are now available in AWS Europe (Ireland) region. U7i instances are part of the AWS 7th generation and are powered by custom fourth-generation Intel Xeon Scalable processors (Sapphire Rapids). U7in-16TB instances offer 16 TiB of DDR5 memory, and U7in-24TB instances offer 24 TiB of DDR5 memory, enabling customers to scale transaction processing throughput in a fast-growing data environment.

Both U7in-16TB and U7in-24TB instances deliver 896 vCPUs and support up to 100 Gbps of Amazon EBS bandwidth for faster data loading and backups, 200 Gbps of network bandwidth, and ENA Express. U7i instances are ideal for customers running mission-critical in-memory databases like SAP HANA, Oracle, and SQL Server.

To learn more about U7i instances, visit the High Memory instances page .

AWS Wickr now offers a managed Data Retention Service feature for Premium users, enabling organizations to retain conversations across their network for data archiving purposes. AWS Wickr is an enterprise-grade, secure collaboration product that provides end-to-end encrypted messaging, file management, screen sharing, and voice/video conferencing capabilities. The Data Retention Service feature provides a cloud-native alternative to traditional container-based data retention methods.

The Data Retention Service can retain conversations in your network, including direct messages and conversations in Groups or Rooms between internal members and external federated teams. The serverless architecture offers simplified deployment, managed infrastructure, automatic scaling, and comprehensive monitoring while maintaining Wickr’s end-to-end encryption standards. This feature is particularly valuable for organizations that require comprehensive data archiving capabilities and maintaining audit trails.

AWS Wickr Premium customers can opt in to enable data retention for their networks. To learn more, visit the AWS Wickr documentation .

We are pleased to announce the availability of Amazon EC2 G6 instances in the AWS GovCloud (US-East) region on Amazon SageMaker AI inference. G6 instances are powered by up to 8 NVIDIA L4 Tensor Core GPUs, each with 24 GB of memory, and third-generation AMD EPYC processors, delivering up to 2x the deep learning inference performance compared to G4dn instances.

With this region expansion, government agencies and organizations operating in GovCloud can deploy inference endpoints on G6 instances to serve generative AI workloads—including small-to-medium language models, image generation, and computer vision tasks—while meeting strict compliance and data residency requirements. G6 instances offer strong price-performance for production inference workloads that fit within 24 GB of GPU memory.

G6 instances for SageMaker AI inference are now available in AWS GovCloud (US-East), in addition to previously supported regions. For pricing information on these instances, please visit our pricing page .

We are pleased to announce the availability of Amazon EC2 G7e instances in Asia Pacific (Seoul), Europe (London), and Asia Pacific (Tokyo) on Amazon SageMaker AI inference. G7e instances feature up to 8 NVIDIA RTX PRO 6000 Blackwell Server Edition GPUs with 96 GB of memory per GPU, 5th Generation Intel Xeon processors, and up to 1,600 Gbps of Elastic Fabric Adapter networking bandwidth, delivering up to 2.3x inference performance compared to previous-generation G6e instances.

With this region expansion, you can now deploy inference endpoints on G7e instances closer to your end users in Asia and Europe, reducing latency for generative AI workloads. G7e instances provide up to 768 GB of total GPU memory on a single instance, enabling you to serve medium-to-large language models of up to 70B parameters with FP8 precision without multi-node configurations. These instances are well suited for LLM inference, image and video generation, spatial computing, and scientific computing workloads that require high GPU memory capacity and bandwidth.

G7e instances for SageMaker AI inference are now available in Asia Pacific (Seoul), Europe (London), and Asia Pacific (Tokyo), in addition to previously supported regions. For pricing information on these instances, please visit our pricing page .

AWS GovCloud (US) now offers Claude Sonnet 5 on Amazon Bedrock. Claude Sonnet 5 delivers strong performance across coding, professional work, and agentic tasks while maintaining the balance of capability, cost, and speed. For coding, it navigates large codebases, lands multi-file changes, and carries debugging and refactoring tasks through to completion with fewer rounds of correction. For agents, it calls tools precisely, holds state across many steps, and recovers from errors so more runs finish correctly the first time. For knowledge work, it builds spreadsheets, drafts documents, and turns unstructured material into structured analysis. 

With this launch, Claude Opus 4.8 and Claude Sonnet 5 are available on bedrock-runtime endpoints in AWS GovCloud (US-West and US-East) and bedrock-mantle endpoints in AWS GovCloud (US-West) for performing inference. Bedrock Mantle, Amazon’s next-generation inference engine, supports the Anthropic Messages API. Amazon Bedrock keeps your data within AWS infrastructure and provides access to Claude Sonnet 5 through a unified service with AWS-managed features like Guardrails, Knowledge Bases, and regional data residency. To learn more, see the Amazon Bedrock documentation and regional availability . 

Today, we’re announcing that Amazon Elastic VMware Service (Amazon EVS) is now available in the Asia Pacific (Seoul), Europe (Zurich), and Europe (Stockholm) Regions. This expansion provides more options to leverage the scale and flexibility of AWS for running your VMware workloads in the cloud.

Amazon EVS lets you run VMware Cloud Foundation (VCF) directly within your Amazon Virtual Private Cloud (VPC) on EC2 bare-metal instances, powered by AWS Nitro. You can set up a complete VCF environment in just a few hours, enabling rapid workload migration to AWS to help you eliminate aging infrastructure, reduce operational risks, and meet critical timelines for exiting your data center. This launch supports all existing Amazon EVS features, including VCF 9.0 and 9.1 support to take advantage of the latest VMware features, such as memory tiering.

The added availability in these Regions gives your VMware workloads lower latency through closer proximity to your end users, compliance with data residency or sovereignty requirements, and additional high availability and resiliency options for your enhanced redundancy strategy.

To get started, visit the Amazon EVS product detail page and user guide .