AWS

Amazon SageMaker AI now supports G7e instances in Asia Pacific (Seoul and Tokyo) and Europe (London). These instances utilize up to eight NVIDIA RTX PRO 6000 Blackwell GPUs and 5th Gen Intel Xeon processors to deliver 2.3x better inference performance than G6e. The expansion allows for lower latency deployment of generative AI workloads closer to users in these regions, supporting models up to 70B parameters.

• G7e instances are now available in Seoul, London, and Tokyo on SageMaker.
• Performance improved up to 2.3x over G6e using Blackwell GPUs and Xeon processors.
• Each instance offers up to 768 GB total GPU memory for large model serving.
• Supports inference endpoints for generative AI models up to 70B parameters.
• Elastic Fabric Adapter provides up to 1,600 Gbps networking bandwidth.

AWS released quarterly security and critical updates for Amazon Corretto LTS and Feature Release versions, including builds for Java 8 through 26. Starting with this release, the default Docker images now utilize Amazon Linux 2023, with AL2 images available as non-default options. Additionally, JavaFX binaries are no longer bundled with Corretto 8, requiring users to follow specific migration recommendations.

• New builds available: 26.0.2, 25.0.4, 21.0.12, 17.0.20, 11.0.32, and 8u502.
• Default Corretto Docker images now use Amazon Linux 2023.
• Amazon Linux 2 images remain available as non-default options.
• JavaFX binaries removed from Corretto 8; check GitHub for migration steps.

Amazon SageMaker AI inference adds support for G7 instances powered by NVIDIA RTX PRO 4500 Blackwell Server Edition GPUs. These instances deliver up to 4.6x AI inference performance compared to previous-generation G6 instances. The hardware includes 32 GB of GPU memory per GPU, 5th Generation Tensor Cores, and up to 700 Gbps of EFA-enabled networking. Local NVMe SSD storage capacity reaches up to 7.6 TB to keep large models close to compute.

• Deploy generative AI models with up to 4.6x inference performance over G6 instances.
• Utilize 32 GB GPU memory per card to reduce the need for aggressive model quantization.
• Leverage 700 Gbps EFA networking for high-throughput multi-GPU distributed inference.
• Store large models on up to 7.6 TB of local NVMe SSD for low-latency access.
• Access 5th Generation Tensor Cores for accelerated AI workloads on SageMaker.

AWS demonstrates constructing a multi-region active-active API by combining Prisma ORM with Amazon Aurora DSQL. The post outlines the architectural patterns required to distribute traffic and manage data consistency across regions. It serves as a practical guide for developers looking to leverage these specific tools for low-latency global applications.

• Prisma ORM pairs with Aurora DSQL to enable active-active multi-region deployments
• Post details architectural patterns for distributing traffic and handling consistency
• Provides a concrete implementation guide for low-latency global application designs