We are pleased to announce general availability of Amazon EC2 P5.4xl instances on SageMaker notebook instances.
Amazon EC2 P5.4xl instances are powered by NVIDIA H100 Tensor Core GPUs and deliver high performance in Amazon EC2 for deep learning (DL) and high performance computing (HPC) applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce cost to train ML models by up to 40%. Customers can use P5 instances for training and deploying complex large language models (LLMs) and diffusion models powering generative AI applications. These applications include question answering, code generation, video and image generation, and speech recognition.
Amazon EC2 P5.4xl instances are available on SageMaker notebook instances in the AWS US East (N. Virginia and Ohio), US West (Oregon), Asia Pacific (Mumbai, Tokyo, Jakarta) and South America (São Paulo) regions.
We are pleased to announce general availability of Amazon EC2 P5.4xl instances on SageMaker notebook instances.
Amazon EC2 P5.4xl instances are powered by NVIDIA H100 Tensor Core GPUs and deliver high performance in Amazon EC2 for deep learning (DL) and high performance computing (HPC) applications. They help you accelerate your time to solution by up to 4x compared to previous-generation GPU-based EC2 instances, and reduce cost to train ML models by up to 40%. Customers can use P5 instances for training and deploying complex large language models (LLMs) and diffusion models powering generative AI applications. These applications include question answering, code generation, video and image generation, and speech recognition.
Amazon EC2 P5.4xl instances are available on SageMaker notebook instances in the AWS US East (N. Virginia and Ohio), US West (Oregon), Asia Pacific (Mumbai, Tokyo, Jakarta) and South America (São Paulo) regions.
Visit developer guides for instructions on setting up and using JupyterLab and CodeEditor applications on SageMaker Studio and SageMaker notebook instances.
We are pleased to announce general availability of Amazon EC2 P5en.48xl instances on SageMaker notebook instances.
Amazon EC2 P5en instances feature 8 H200 GPUs which have 1.7x GPU memory size and 1.4x GPU memory bandwidth than H100 GPUs featured in P5 instances. P5en instances pair the H200 GPUs with high performance custom 4th Generation Intel Xeon Scalable processors, enabling Gen5 PCIe between CPU and GPU which provides up to 4x the bandwidth between CPU and GPU and boosts AI training and inference performance. P5en, with up to 3200 Gbps of third generation of EFA using Nitro v5, shows up to 35% improvement in latency compared to P5 that uses the previous generation of EFA and Nitro. This helps improve collective communications performance for distributed training workloads such as deep learning, generative AI, real-time data processing, and high-performance computing (HPC) applications.
Amazon EC2 P5en.48xl instances are available on SageMaker notebook instances in the AWS US East (N. Virginia and Ohio), US West (Oregon), and Asia Pacific (Tokyo) regions.
We are pleased to announce general availability of Amazon EC2 P5en.48xl instances on SageMaker notebook instances.
Amazon EC2 P5en instances feature 8 H200 GPUs which have 1.7x GPU memory size and 1.4x GPU memory bandwidth than H100 GPUs featured in P5 instances. P5en instances pair the H200 GPUs with high performance custom 4th Generation Intel Xeon Scalable processors, enabling Gen5 PCIe between CPU and GPU which provides up to 4x the bandwidth between CPU and GPU and boosts AI training and inference performance. P5en, with up to 3200 Gbps of third generation of EFA using Nitro v5, shows up to 35% improvement in latency compared to P5 that uses the previous generation of EFA and Nitro. This helps improve collective communications performance for distributed training workloads such as deep learning, generative AI, real-time data processing, and high-performance computing (HPC) applications.
Amazon EC2 P5en.48xl instances are available on SageMaker notebook instances in the AWS US East (N. Virginia and Ohio), US West (Oregon), and Asia Pacific (Tokyo) regions.
Visit developer guides for instructions on setting up and using JupyterLab and CodeEditor applications on SageMaker Studio and SageMaker notebook instances.
Amazon EMR now supports Apache Spark 4.0.2 across all three deployment models. With Spark 4.0.2, you can build and maintain data pipelines more easily with ANSI SQL and VARIANT data types, enforce fine-grained access control (FGAC) at the row level or column level, strengthen compliance and governance frameworks with Apache Iceberg v3 table format, and deploy new real-time applications faster with enhanced streaming capabilities.
With Spark 4.0.2, you can build data pipelines, making data engineering accessible to a broader range of users through standard ANSI SQL support, eliminating the need to learn Spark-specific syntax. Spark 4.0.2 natively supports JSON and semi-structured data through VARIANT data types, providing flexibility for handling diverse data formats. You can enforce fine-grained access control (FGAC) on both read and write operations for AWS Lake Formation registered tables in your Apache Spark jobs. Building on these security capabilities, Apache Iceberg v3 table format provides stronger transaction guarantees and tracks data lineage, creating the audit trails required for regulatory compliance. Enhanced streaming controls simplify management of complex stateful operations and improve monitoring, enabling you to deploy real-time applications for fraud detection, personalization, and other time-sensitive use cases faster.
Apache Spark 4.0.2 is available in all regions where EMR is available. If you are upgrading your existing EMR application, you can use Apache Spark upgrade agent to accelerate your upgrades. To learn more about Apache Spark 4.0.2 on Amazon EMR, visit the Amazon EMR release notes, or get started by creating an EMR application with Spark 4.0.2 from the AWS Management Console.
Amazon EMR now supports Apache Spark 4.0.2 across all three deployment models. With Spark 4.0.2, you can build and maintain data pipelines more easily with ANSI SQL and VARIANT data types, enforce fine-grained access control (FGAC) at the row level or column level, strengthen compliance and governance frameworks with Apache Iceberg v3 table format, and deploy new real-time applications faster with enhanced streaming capabilities. With Spark 4.0.2, you can build data pipelines, making data engineering accessible to a broader range of users through standard ANSI SQL support, eliminating the need to learn Spark-specific syntax. Spark 4.0.2 natively supports JSON and semi-structured data through VARIANT data types, providing flexibility for handling diverse data formats. You can enforce fine-grained access control (FGAC) on both read and write operations for AWS Lake Formation registered tables in your Apache Spark jobs. Building on these security capabilities, Apache Iceberg v3 table format provides stronger transaction guarantees and tracks data lineage, creating the audit trails required for regulatory compliance. Enhanced streaming controls simplify management of complex stateful operations and improve monitoring, enabling you to deploy real-time applications for fraud detection, personalization, and other time-sensitive use cases faster.
Apache Spark 4.0.2 is available in all regions where EMR is available. If you are upgrading your existing EMR application, you can use Apache Spark upgrade agent to accelerate your upgrades. To learn more about Apache Spark 4.0.2 on Amazon EMR, visit the Amazon EMR release notes, or get started by creating an EMR application with Spark 4.0.2 from the AWS Management Console.
Amazon Connect Customer now enables managers to use generative AI to automatically evaluate self-service interactions, and get aggregated insights to help improve customer experience. Managers can define custom evaluation criteria in natural language within evaluation forms — such as «Were all of the customer issues resolved by the AI agent?» — which generative AI uses to help assess the quality of the self-service interaction. Connect provides detailed reasoning for the evaluation along with relevant reference points from the conversation transcript. Managers can review these insights in aggregate and on individual contacts, alongside self-service interaction recordings and transcripts, to identify opportunities to improve AI agent performance.
This feature is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Europe (Frankfurt). To learn more, please visit our documentation and our webpage. For information about Amazon Connect Customer pricing, please visit our pricing page.
Amazon Connect Customer now enables managers to use generative AI to automatically evaluate self-service interactions, and get aggregated insights to help improve customer experience. Managers can define custom evaluation criteria in natural language within evaluation forms — such as «Were all of the customer issues resolved by the AI agent?» — which generative AI uses to help assess the quality of the self-service interaction. Connect provides detailed reasoning for the evaluation along with relevant reference points from the conversation transcript. Managers can review these insights in aggregate and on individual contacts, alongside self-service interaction recordings and transcripts, to identify opportunities to improve AI agent performance.
This feature is available in the following AWS Regions: US East (N. Virginia), US West (Oregon), Asia Pacific (Seoul), Asia Pacific (Singapore), Asia Pacific (Sydney), Asia Pacific (Tokyo), and Europe (Frankfurt). To learn more, please visit our documentation and our webpage. For information about Amazon Connect Customer pricing, please visit our pricing page.
Amazon SageMaker HyperPod now supports minimum capacity requirements (MinCount) for clusters using Slurm orchestration with continuous provisioning. With continuous provisioning, HyperPod provisions clusters with available partial capacity so you can start your AI/ML jobs quickly, while continuing to provision remaining instances asynchronously in the background. While this provides flexibility, some training workloads require a guaranteed minimum number of nodes before they can start effectively. MinCount lets you specify the minimum number of instances that must be successfully provisioned before an instance group transitions to InService status, giving you greater control over when your cluster becomes available for job scheduling.
This is particularly useful for distributed training workloads using frameworks such as PyTorch FSDP, Megatron-LM, or NVIDIA NeMo, where training jobs are commonly configured with a fixed number of participating nodes and may not start efficiently or correctly with partial cluster capacity. It also benefits teams that need to guarantee a baseline GPU count to meet SLA or cost-efficiency targets before committing to a training run.
You can specify MinInstanceCount in the CreateCluster or UpdateCluster API request to set a minimum capacity threshold for an instance group. The instance group remains in Creating or Updating status until the threshold is met, then transitions to InService and nodes become available for Slurm job scheduling. HyperPod continues launching additional instances beyond MinCount until the target count is reached. If MinCount cannot be satisfied within 3 hours, the system automatically rolls back the instance group to its last known good state.
MinCount for Slurm clusters with continuous provisioning is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To get started on specifying minimum capacity requirements for your cluster, see Minimum capacity requirements (MinCount) in the Amazon SageMaker AI documentation.
Amazon SageMaker HyperPod now supports minimum capacity requirements (MinCount) for clusters using Slurm orchestration with continuous provisioning. With continuous provisioning, HyperPod provisions clusters with available partial capacity so you can start your AI/ML jobs quickly, while continuing to provision remaining instances asynchronously in the background. While this provides flexibility, some training workloads require a guaranteed minimum number of nodes before they can start effectively. MinCount lets you specify the minimum number of instances that must be successfully provisioned before an instance group transitions to InService status, giving you greater control over when your cluster becomes available for job scheduling. This is particularly useful for distributed training workloads using frameworks such as PyTorch FSDP, Megatron-LM, or NVIDIA NeMo, where training jobs are commonly configured with a fixed number of participating nodes and may not start efficiently or correctly with partial cluster capacity. It also benefits teams that need to guarantee a baseline GPU count to meet SLA or cost-efficiency targets before committing to a training run. You can specify MinInstanceCount in the CreateCluster or UpdateCluster API request to set a minimum capacity threshold for an instance group. The instance group remains in Creating or Updating status until the threshold is met, then transitions to InService and nodes become available for Slurm job scheduling. HyperPod continues launching additional instances beyond MinCount until the target count is reached. If MinCount cannot be satisfied within 3 hours, the system automatically rolls back the instance group to its last known good state. MinCount for Slurm clusters with continuous provisioning is available in all AWS Regions where Amazon SageMaker HyperPod is supported. To get started on specifying minimum capacity requirements for your cluster, see Minimum capacity requirements (MinCount) in the Amazon SageMaker AI documentation.
Hoy anunciamos MAI-Image-2.5, que ocupa el tercer puesto en la clasificación texto a imagen de Arena.
Es nuestro modelo de imagen más sólido hasta la fecha y el siguiente paso en la serie MAI-Image.
MAI-Image-2.5 se desempeña bien en una amplia variedad de estilos, sigue las instrucciones de cerca, renderiza el texto de manera más fiable que nunca y produce imágenes detalladas y coherentes, tal y como ustedes lo pretenden.
Esto supone un cambio radical en la calidad respecto a nuestro modelo anterior, MAI-Image-2, que ofrece mejoras importantes en el renderizado de texto, ilustración estilizada e imágenes comerciales.
El modelo también muestra un fuerte razonamiento visual a través de objetos, estructura de escena, iluminación, escala y relaciones espaciales, para ayudar a convertir indicaciones simples en imágenes pulidas.
Texto más nítido, un trabajo de marca más fuerte
El trabajo creativo de nivel profesional requiere acertar con cada detalle: las palabras de un cartel, la etiqueta del embalaje, la estructura de una foto de producto, la forma en que la luz cae sobre una escena.
MAI-Image-2.5 logra avances significativos en las áreas que convierten las imágenes de impresionantes en usables, en especial en el renderizado de texto y en conceptos de producto y marca.
Las palabras son más agudas. Los diseños se mantienen mejor juntos. Las escenas se sienten más deliberadas. Los gráficos más marcados se perciben con más pulido.
Más por venir
MAI-Image-2.5 continúa el impulso de MAI-Image-1 y MAI-Image-2: mayor calidad, mayor rango y resultados más fiables para trabajos creativos reales.
Pruébenlo hoy en Arena, también llegará al MAI Playground and Foundry en las próximas semanas.
Ciudad de México — En el marco de su 40 aniversario en el país, Microsoft México refrendó su compromiso por el desarrollo tecnológico nacional con foco en infraestructura de nube, adopción de inteligencia artificial y formación de talento, en un momento en que estas capacidades se perfilan como ejes clave para la competitividad y el crecimiento económico. Desde su llegada en 1986, como la primera subsidiaria de la compañía en Latinoamérica, el mercado mexicano se convirtió en un motor para su expansión global y un referente regional en innovación.
Este compromiso se ha visto reflejado en los últimos 5 años, ya que, durante sus visitas en 2019 y 2024, Satya Nadella, CEO de Microsoft, anunció una inversión total de 2.4 mil millones de dólares en México para fortalecer la infraestructura de nube, una región de centros de datos, además de impulsar programas de skilling que permitan democratizar el acceso a habilidades digitales y de IA en el país. Hoy en día, se ha capacitado a más de 3.4millones de mexicanos pertenecientes a diferentes instituciones académicas, organizaciones de la sociedad civil, así como la diferentes cámaras y asociaciones empresariales; con el objetivo de preparar a personas y organizaciones para una nueva era tecnológica impulsada por la inteligencia artificial.
En estas cuatro décadas, Microsoft ha consolidado una presencia robusta en México con tres oficinas (Ciudad de México, Querétaro y Monterrey), más de 1,000 colaboradores y talento distribuido en 21 estados de la República. Su operación en el país también incluye una región de centros de datos en Querétaro, un Innovation Hub en Ciudad de México y un ecosistema de más de 7,000 socios, lo que refleja la escala de su presencia y su capacidad para impulsar innovación, desarrollo tecnológico y adopción de inteligencia artificial en distintos sectores.
Por otro lado, a través de la creación de su región de centros de datos, Microsoft ha apoyado a la comunidad del municipio de Colón en Querétaro mediante la rehabilitación de la Plaza Galeras, un espacio de encuentro y convivencia fundamental para la comunidad. El proyecto, desarrollado junto con autoridades locales y organizaciones de responsabilidad ambiental, permitió recuperar un punto emblemático para más de 3,500 habitantes brindando mejoras en infraestructura y acciones de sostenibilidad, fortaleciendo el tejido social y los espacios públicos para la población.
La compañía tecnológica más importante a nivel global trabaja de la mano con empresas e instituciones académicas mexicanas que ya están transformando la manera en que operan y generan valor. Cemex, por ejemplo, desarrolló LUCA Bot, un agente de IA creado sobre tecnología de Microsoft que permite optimizar procesos internos y mejorar la toma de decisiones. En el sector educativo, el Tecnológico de Monterrey incorpora soluciones de IA generativa para potenciar el aprendizaje, la productividad y la innovación dentro de su comunidad. Por su parte, Nemak implementa herramientas de IA y analítica avanzada sobre la nube de Microsoft para fortalecer su competitividad y acelerar su transformación digital.
“Al mirar estos primeros 40 años, nos sentimos orgullosos de haber crecido de la mano con México y nos entusiasma seguir haciéndolo por muchos años más. Como compañía, ponemos la innovación en el centro de todo lo que hacemos, con un propósito definido, que la tecnología genere un impacto positivo en las personas. Esa es la historia que hemos construido junto al país, marcada por el talento, la creatividad y el empuje de su gente”, señaló Rafael Sánchez Loza, Presidente y Director General de Microsoft México.
A 40 años de iniciar operaciones en el país, Microsoft México continúa enfocado en acompañar a empresas, instituciones y gobiernos en su camino hacia el futuro digital, impulsando la innovación, el desarrollo de talento y el crecimiento sostenible, con la inteligencia artificial como un motor estratégico para el progreso de México.
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Acerca de Microsoft
Microsoft (Nasdaq «MSFT» @microsoft) crea plataformas y herramientas impulsadas por la IA para ofrecer soluciones innovadoras que satisfagan las necesidades cambiantes de nuestros clientes. La empresa de tecnología está comprometida con hacer que la IA esté ampliamente disponible, de manera responsable, con la misión de empoderar a cada persona y a cada organización en el planeta para lograr más.
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) X8i instances are available in the Asia Pacific (Singapore), Asia Pacific (Sydney) and AWS GovCloud (US-West) 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
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) X8i instances are available in the Asia Pacific (Singapore), Asia Pacific (Sydney) and AWS GovCloud (US-West) 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
Starting today, Amazon EC2 M8i and M8i-flex instances are now available in AWS GovCloud (US-East)Region. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The M8i and M8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver up to 20% better performance than M7i and M7i-flex instances, with even higher gains for specific workloads. The M8i and M8i-flex instances are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to M7i and M7i-flex instances.
M8i-flex are the easiest way to get price performance benefits for a majority of general-purpose workloads like web and application servers, microservices, small and medium data stores, virtual desktops, and enterprise applications. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources.
M8i instances are a great choice for all general purpose workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. The SAP-certified M8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications.
Starting today, Amazon EC2 M8i and M8i-flex instances are now available in AWS GovCloud (US-East) Region. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The M8i and M8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver up to 20% better performance than M7i and M7i-flex instances, with even higher gains for specific workloads. The M8i and M8i-flex instances are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to M7i and M7i-flex instances. M8i-flex are the easiest way to get price performance benefits for a majority of general-purpose workloads like web and application servers, microservices, small and medium data stores, virtual desktops, and enterprise applications. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources. M8i instances are a great choice for all general purpose workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. The SAP-certified M8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications. To get started, sign in to the AWS Management Console. For more information about the new instances, visit the M8i and M8i-flex instance page or visit the AWS News blog.
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8i and R8i-flex instances are available in the AWS GovCloud (US-East) Region. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The R8i and R8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver 20% higher performance than R7i instances, with even higher gains for specific workloads. They are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to R7i.
R8i-flex, our first memory-optimized Flex instances, are the easiest way to get price performance benefits for a majority of memory-intensive workloads. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources.
R8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. R8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications. R8i instances are SAP-certified and deliver 142,100 aSAPS, delivering exceptional performance for mission-critical SAP workloads.
Starting today, Amazon Elastic Compute Cloud (Amazon EC2) R8i and R8i-flex instances are available in the AWS GovCloud (US-East) Region. These instances are powered by custom Intel Xeon 6 processors, available only on AWS, delivering the highest performance and fastest memory bandwidth among comparable Intel processors in the cloud. The R8i and R8i-flex instances offer up to 15% better price-performance, and 2.5x more memory bandwidth compared to previous generation Intel-based instances. They deliver 20% higher performance than R7i instances, with even higher gains for specific workloads. They are up to 30% faster for PostgreSQL databases, up to 60% faster for NGINX web applications, and up to 40% faster for AI deep learning recommendation models compared to R7i. R8i-flex, our first memory-optimized Flex instances, are the easiest way to get price performance benefits for a majority of memory-intensive workloads. They offer the most common sizes, from large to 16xlarge, and are a great first choice for applications that don’t fully utilize all compute resources. R8i instances are a great choice for all memory-intensive workloads, especially for workloads that need the largest instance sizes or continuous high CPU usage. R8i instances offer 13 sizes including 2 bare metal sizes and the new 96xlarge size for the largest applications. R8i instances are SAP-certified and deliver 142,100 aSAPS, delivering exceptional performance for mission-critical SAP workloads. To get started, sign in to the AWS Management Console. For more information about the R8i and R8i-flex instances visit the AWS News blog.
