Starting today, Amazon Elastic Compute Cloud (Amazon EC2) X8i instances are available in the Europe (Milan) and Europe (Spain) regions. These instances are powered by custom Intel Xeon 6 processors available only on AWS. X8i instances are SAP-certified and deliver the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. They deliver up to 43% higher performance, 1.5x more memory capacity (up to 6TB), and 3.3x more memory bandwidth compared to previous generation X2i instances. 
 
X8i instances are designed for memory-intensive workloads like SAP HANA, large databases, data analytics, and Electronic Design Automation (EDA). Compared to X2i instances, X8i instances offer up to 50% higher SAPS performance, up to 47% faster PostgreSQL performance, 88% faster Memcached performance, and 46% faster AI inference performance. X8i instances come in 14 sizes, from large to 96xlarge, including two bare metal options. 
 
To get started, visit the AWS Management Console . X8i instances can be purchased via Savings Plans, On-Demand instances, and Spot instances. For more information visit X8i instances page .

Amazon Redshift now integrates with the Agent Toolkit for AWS , enabling you to build, query, troubleshoot, and migrate to Amazon Redshift data warehouses and data lakes directly from AI agents such as Claude Code, Kiro, and Cursor. The integration pairs the AWS MCP (Model Context Protocol) server , which provides authenticated AWS API execution on your behalf — with Redshift skills : curated packages of tested procedures and reference material that help AI agents complete Redshift tasks more effectively.

The Redshift skills cover SQL syntax references to reduce query generation errors, metadata discovery to explore schemas and data without writing SQL by hand, data loading patterns, materialized view best practices, function and data type guidance, and extensions such as Qualify, Pivot, and Super. It also guides end-to-end data warehouse migrations to Amazon Redshift, including discovery, schema and SQL conversion, data movement, validation, and performance comparison. We will continue to expand these skills with additional capabilities over time.

The skills work with provisioned clusters and Serverless workgroups, require no changes to existing infrastructure, and are available at no additional charge in all AWS Regions where Amazon Redshift and the AWS MCP Server are offered.

To get started, install the aws-data-analytics plugin in your agent, which bundles the MCP Server configuration and Redshift skills in a single step. Agents with MCP Server access can also discover and load skills at runtime without pre-installation. For setup instructions, see the Agent Toolkit documentation or the Amazon Redshift skills documentation .

Amazon Redshift  now supports Amazon Kinesis Data Streams (KDS) record sizes up to 10 MiB—a 10x increase from the previous 1 MiB limit—fully matching the expanded maximum record size in Amazon KDS. This means you can stream significantly larger payloads directly into Amazon Redshift without splitting records, simplifying your ingestion pipelines and unlocking new use cases for high-volume, large-record workloads.

Amazon Redshift support for 10MiB record size in Amazon KDS streams is now available in all commercial AWS regions where Amazon Redshift is available. For more information on direct streaming ingestion into Amazon Redshift, see the Amazon Redshift streaming documentation . For more information on 10MiB record support in Amazon KDS, see the Amazon KDS documentation .  

Meta’s Muse-Glimmer-30B and Alibaba’s Qwen 3.8-27B models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These two models bring specialized capabilities spanning autonomous local agentic workflows and multimodal long-horizon reasoning, enabling customers to deploy high-performance, scalable AI solutions on AWS infrastructure.

These models address different enterprise AI challenges with specialized capabilities:

Muse-Glimmer-30B is engineered for autonomous agentic tasks with multi-step reasoning, tool use, and failure recovery. This 30B-parameter dense model from Meta Superintelligence Lab combines a dedicated ~1.8B ViT-G/14 perception encoder with interleaved text and image inputs, a 131K+ context window, and selectable reasoning strength (low through extra-high). Released under Apache 2.0, it handles sequential tool calls, recovers from failures, and operates entirely without cloud infrastructure which is ideal for always-on enterprise agents.

Qwen 3.8-27B excels in coding, multi-step agentic tasks, and multimodal understanding across text, images, and video. A dense 27B-parameter native vision-language model with a 262K context window (extendable to ~1M via YaRN scaling), it delivers substantial gains over its predecessor with adjustable reasoning effort levels. Scoring 61.7 on SWE-bench Pro and running at ~17GB quantized, it carries complex multi-step tasks through to completion with greater reliability.

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 .

NVIDIA’s Cosmos3-Edge, Cosmos3-Nano, and Cosmos3-Super models are now available on Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. These three models form the Cosmos 3 family of open, frontier omnimodal world models for physical AI, enabling customers to build robots, autonomous vehicles, and vision AI that perceive, reason, plan, and act in the physical world.

These models address different physical AI challenges with specialized capabilities:

Cosmos3-Edge is engineered for on-device robot control and real-time visual reasoning on edge hardware. This 4B-parameter omni-model (with a 2B Nemotron-based reasoner) operates at robot-control resolution (640×360), delivering real-time reasoning and generating 32 actions per inference at 15 Hz on NVIDIA Jetson Thor. It supports 256p and 480p video at 12–30 FPS, bringing frontier physical AI capabilities directly to embedded systems.

Cosmos3-Nano excels in physics-aware world generation and physical reasoning as a compact 16B-parameter omnimodal model. It processes combinations of text, image, video, audio, and action trajectories to produce corresponding outputs, enabling robots and vision AI agents to reason using prior knowledge, physics understanding, and common sense. It supports chain-of-thought reasoning over text, images, and video with resolutions up to 720p.

Cosmos3-Super provides the highest-fidelity world generation and simulation in the Cosmos 3 family at 64B parameters. It jointly processes and generates language, images, video, audio, and action sequences within a unified Mixture-of-Transformers architecture, supporting resolutions up to 720p across multiple aspect ratios. Ideal for large-scale simulation, synthetic data generation, and policy learning workflows.

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 .

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