Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x). Faster service auto scaling also enables you to reduce baseline capacity and lower compute costs while maintaining service reliability and performance as workload demand fluctuates.
Amazon ECS service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics. With today’s launch, target tracking policies for CPU and memory utilization now support 20-second metric resolution, in addition to the default 60-second resolution, for faster scaling signal detection. To get started, use the AWS Console, CLI, CloudFormation, or AWS SDKs to configure 20-second resolution for CPU or memory utilization metrics when creating or updating your ECS service, then configure a target tracking policy selecting the corresponding high-resolution predefined metric.
This feature is available in all AWS commercial and AWS GovCloud (US) Regions, across all ECS compute options: AWS Fargate, Amazon ECS Managed Instances, and Amazon EC2. High-resolution metrics are subject to standard CloudWatch charges; for a pricing example, see Amazon CloudWatch pricing. To learn more, see our documentation and the launch blog post.
Amazon ECS service auto scaling now detects and responds to load changes faster with support for high resolution (20-second) metrics and metric publishing optimizations. In AWS benchmarking tests, time to trigger scale-out improved from 363 seconds to 86 seconds (76% faster, 4.2x), and total time to scale and provision new tasks improved from 386 seconds to 109 seconds (72% faster, 3.5x). Faster service auto scaling also enables you to reduce baseline capacity and lower compute costs while maintaining service reliability and performance as workload demand fluctuates. Amazon ECS service auto scaling automatically adjusts task counts to meet workload demand with comprehensive scaling policies, including predictive scaling for recurring traffic patterns, scheduled scaling for planned events, and target tracking to scale dynamically on real-time metrics. With today’s launch, target tracking policies for CPU and memory utilization now support 20-second metric resolution, in addition to the default 60-second resolution, for faster scaling signal detection. To get started, use the AWS Console, CLI, CloudFormation, or AWS SDKs to configure 20-second resolution for CPU or memory utilization metrics when creating or updating your ECS service, then configure a target tracking policy selecting the corresponding high-resolution predefined metric. This feature is available in all AWS commercial and AWS GovCloud (US) Regions, across all ECS compute options: AWS Fargate, Amazon ECS Managed Instances, and Amazon EC2. High-resolution metrics are subject to standard CloudWatch charges; for a pricing example, see Amazon CloudWatch pricing. To learn more, see our documentation and the launch blog post.
Today, AWS announced the availability of Ministral-3-14B-Instruct-2512 in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This model from Mistral AI delivers frontier-class multimodal capabilities in a compact 14B-parameter architecture optimized for edge deployment, enabling customers to build advanced AI assistants, agentic systems, and vision-enabled applications on AWS infrastructure.
Ministral-3-14B-Instruct excels at analyzing images and providing insights based on visual content in addition to text, agentic capabilities with native function calling and JSON output, and multilingual understanding across dozens of languages including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic.
With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Today, AWS announced the availability of Ministral-3-14B-Instruct-2512 in Amazon SageMaker JumpStart, expanding the portfolio of foundation models available to AWS customers. This model from Mistral AI delivers frontier-class multimodal capabilities in a compact 14B-parameter architecture optimized for edge deployment, enabling customers to build advanced AI assistants, agentic systems, and vision-enabled applications on AWS infrastructure.
Ministral-3-14B-Instruct excels at analyzing images and providing insights based on visual content in addition to text, agentic capabilities with native function calling and JSON output, and multilingual understanding across dozens of languages including English, French, Spanish, German, Chinese, Japanese, Korean, and Arabic.
With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Today, AWS announced the availability of all-MiniLM-L12-v2 in Amazon SageMaker JumpStart, expanding the portfolio of models available to AWS customers. This model from Sentence Transformers maps sentences and paragraphs to a 384-dimensional dense vector space, enabling customers to build high-quality semantic search, text clustering, and sentence similarity applications on AWS infrastructure.
all-MiniLM-L12-v2 excels at encoding sentences and short paragraphs into dense vector representations that capture semantic meaning, making it ideal for information retrieval, semantic search systems, document clustering, duplicate detection, and paraphrase identification. Its compact architecture delivers fast inference while maintaining strong embedding quality, well suited for production workloads that require efficient text representations at scale.
With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Today, AWS announced the availability of all-MiniLM-L12-v2 in Amazon SageMaker JumpStart, expanding the portfolio of models available to AWS customers. This model from Sentence Transformers maps sentences and paragraphs to a 384-dimensional dense vector space, enabling customers to build high-quality semantic search, text clustering, and sentence similarity applications on AWS infrastructure.
all-MiniLM-L12-v2 excels at encoding sentences and short paragraphs into dense vector representations that capture semantic meaning, making it ideal for information retrieval, semantic search systems, document clustering, duplicate detection, and paraphrase identification. Its compact architecture delivers fast inference while maintaining strong embedding quality, well suited for production workloads that require efficient text representations at scale.
With SageMaker JumpStart, customers can deploy this model with just a few clicks to address their specific AI use cases. To get started with this model, navigate to the Models section of SageMaker Studio or use the SageMaker Python SDK to deploy the model to your AWS account. For more information about deploying and using foundation models in SageMaker JumpStart, see the Amazon SageMaker JumpStart documentation.
Starting today, Nested virtualization is now available on additional Intel platforms and additional Regions. Nested virtualization is now available on C7i,R7i, M7i, C7id,R7id, M7id, C7i-flex,R7i-flex, M7i-flex, I7i, C8i-flex,R8i-flex, M8i-flex,and X8i, in addition to already available support on C8i, M8i and R8i instances. This capability is also now available in US GovCloud (US-East) and US GovCloud (US-West), in addition to existing support in all commercial regions. With nested virtualization capabilities, customers can create nested environments by running KVM or Hyper-V on virtual EC2 instances. Customers can leverage this capability for use cases such as running emulators for mobile applications, simulating in-vehicle hardware for automobiles, and running Windows Subsystem for Linux on Windows workstations. To learn more see documentation .
Starting today, Nested virtualization is now available on additional Intel platforms and additional Regions. Nested virtualization is now available on C7i,R7i, M7i, C7id,R7id, M7id, C7i-flex,R7i-flex, M7i-flex, I7i, C8i-flex,R8i-flex, M8i-flex,and X8i, in addition to already available support on C8i, M8i and R8i instances. This capability is also now available in US GovCloud (US-East) and US GovCloud (US-West), in addition to existing support in all commercial regions. With nested virtualization capabilities, customers can create nested environments by running KVM or Hyper-V on virtual EC2 instances. Customers can leverage this capability for use cases such as running emulators for mobile applications, simulating in-vehicle hardware for automobiles, and running Windows Subsystem for Linux on Windows workstations. To learn more see documentation .
Amazon Connect Customer now supports the ability to interrupt an agent with a contact, overriding their usual routing configuration in case of urgent or time-sensitive work. For example, an agent may be waiting for a time-sensitive callback on their personal extension, while taking customer service calls in the meantime. When that urgent call comes in, it can now ring the agent even if the agent is currently already on another call, so the agent can decide whether to put the first caller on hold to pick up the callback as well.
You can also use this feature to directly assign certain contacts to a specific agent even though that agent has set themselves to a custom status where they normally could not be offered queued contacts. For example, you may want to ensure that a specific agent cannot take customer service calls while in “Back Office Work” but still allow calls to their personal extension to ring through, improving efficiency for urgent contacts.
Amazon Connect Customer now supports the ability to interrupt an agent with a contact, overriding their usual routing configuration in case of urgent or time-sensitive work. For example, an agent may be waiting for a time-sensitive callback on their personal extension, while taking customer service calls in the meantime. When that urgent call comes in, it can now ring the agent even if the agent is currently already on another call, so the agent can decide whether to put the first caller on hold to pick up the callback as well. You can also use this feature to directly assign certain contacts to a specific agent even though that agent has set themselves to a custom status where they normally could not be offered queued contacts. For example, you may want to ensure that a specific agent cannot take customer service calls while in “Back Office Work” but still allow calls to their personal extension to ring through, improving efficiency for urgent contacts. This feature is available in all AWS regions where Amazon Connect Customer is offered. To learn more about this feature, see the Amazon Connect Customer Administrator Guide. To learn more about Amazon Connect Customer, the AWS cloud-based contact center, please visit the Amazon Connect Customer website.
¿Por qué los medicamentos contra el cáncer no funcionan igual para todo el mundo?
Por: Susanna Ray, escritora de Microsoft.
El tratamiento del cáncer se ha vuelto más preciso con el tiempo, ya que los médicos clasificaron primero la enfermedad según su origen en el cuerpo y, de manera más reciente, por las mutaciones encontradas en las células cancerosas para ayudar a encontrar los fármacos adecuados para tratarla.
Pero, ¿por qué dos personas con cánceres en apariencia similares pueden responder de manera tan diferente al mismo medicamento? El investigador de Microsoft, Lorin Crawford, cree que la respuesta está en cómo se comportan en realidad los tumores, no solo en cómo se categorizan.
Un estudio de Crawford y su equipo, publicado . en Nature Methods, supone un paso importante para ayudar a la IA a entender cómo actúan e interactúan las células individuales con su entorno, ya que los investigadores aprovechan el poder de la tecnología para detectar patrones que los enfoques tradicionales pueden pasar por alto.
La investigación forma parte del Proyecto Ex Vivo, una colaboración entre Microsoft y el Broad Institute con el apoyo del Dana-Farber Cancer Institute. El trabajo del grupo tiene como objetivo incluir el comportamiento celular en la categorización y tratamiento del cáncer, para ayudar a combatir una de las principales causas de muerte a nivel mundial, al emparejar con mayor éxito las terapias con los pacientes.
«La complejidad de la enfermedad es muy interesante a nivel científico, pero también una en la que puedes tener un impacto casi inmediato», dice Crawford. «Siento que hago algo más grande que yo. Cualquier hallazgo parece un paso adelante de alguna manera.»
Mirar más allá de las mutaciones
Parte del reto de la investigación sobre el cáncer es que los científicos pueden perder señales clave cuando prueban fármacos fuera del cuerpo. Los modelos ex vivo — células cancerosas cultivadas en laboratorios, incluidos minitumores llamados organoides — no siempre coinciden con lo que ocurre dentro de una persona. Eso significa que un medicamento que parece prometedor en una placa de Petri puede quedarse corto en un paciente.
El equipo del Proyecto Ex Vivo se centra en el «estado celular» — cómo se comportan y responden las células cancerosas a su entorno. Los estados celulares pueden influir en a qué tratamientos es sensible un tumor, a qué velocidad se desarrolla la resistencia a los fármacos y a qué tan agresiva se vuelve la enfermedad.
En el cáncer de páncreas, por ejemplo, los investigadores han observado dos estados celulares amplios asociados con diferentes resultados y respuestas al tratamiento. Pero cuando las células tumorales se cultivan en laboratorio, dice Crawford, los modelos a menudo reflejan solo uno de esos estados, lo que puede hacer que los resultados de laboratorio no coincidan con lo que ocurre en los pacientes.
«Tú y yo podemos tener la misma mutación, pero estados celulares diferentes por completo, y eso es lo que en verdad importa más adelante», dice Crawford.
Menos desajustes, mejores apuestas
Si el estado celular puede medirse de manera fiable, Crawford cree que podría cambiar el tratamiento del cáncer de dos maneras clave.
En primer lugar, podría mejorar la forma en que los pacientes se emparejan con terapias existentes y se inscriben en ensayos clínicos. Una forma más matizada de agrupar los tumores podría aumentar las probabilidades de que un tratamiento se pruebe y en verdad tenga posibilidades de funcionar.
En segundo lugar, podría abrir un nuevo camino para el propio desarrollo de fármacos. En lugar de dirigirse a una mutación, los investigadores podrían intentar atacar —o incluso cambiar— el estado subyacente de un tumor, empujándolo hacia una forma más fácil de tratar.
«El reto como clínico es entender qué características dentro del tumor de un paciente tal vez influirán en su comportamiento y respuestas a las terapias con el tiempo», dice Srivatsan Raghavan, oncólogo médico y médico-científico en Dana-Farber y codirector del Proyecto Ex Vivo. «Esta investigación pretende representar mejor estas diversas características en los modelos de cáncer y producir pruebas que reflejen la complejidad de los comportamientos tumorales que observamos en los pacientes.»
Un estadístico en un laboratorio húmedo
Lorin Crawford. (Foto de Gregory Winter)
Crawford tomó un camino poco común en esta investigación.
Formado como matemático y estadístico, un asesor le instó a mirar más allá de las hojas de cálculo y aprender cómo se creaban los datos. Pasó la mayor parte de su trabajo de posgrado en la Universidad de Duke, donde cruzaba dos mundos, inmerso en un «laboratorio húmedo» de biología del cáncer donde los investigadores trabajaban de manera directa con células vivas, no solo con datos.
Aunque al principio parecía «estar en un país extranjero», Crawford dice que «tal vez fue la mejor decisión que he tomado nunca, desde el punto de vista profesional.»
Se dio cuenta de que su formación podría ayudar a los biólogos a gestionar la enorme cantidad de datos que se utilizan para comprender las variaciones entre pacientes y subtipos de cáncer.
«Las cosas empezaron a encajar para mí», dice.
Esa experiencia moldeó su manera de pensar hoy en día sobre el Proyecto Ex Vivo — como una manera de cerrar la brecha entre lo que funciona en el laboratorio y lo que en verdad ayuda a los pacientes. Expertos computacionales y experimentadores se sientan a resolver problemas juntos, para utilizar herramientas de IA y muestras de tumores de pacientes anónimos.
Desde un problema de laboratorio hasta la transformación del tratamiento del cáncer
Lo que comenzó como un esfuerzo de investigación enfocado en 2022 se ha expandido con rapidez, impulsado por los avances en IA. El equipo del Proyecto Ex Vivo utiliza modelos computacionales para realizar experimentos virtuales e identificar las hipótesis más prometedoras antes de invertir tiempo y dinero en el laboratorio. Las herramientas de IA pueden ayudar a predecir cómo un fármaco podría cambiar de un estado a otro, o cómo los estados se traducen entre diferentes tipos de cáncer.
Como muestran Crawford y sus colegas en el estudio de Nature Methods, los modelos de IA aprenden más al observar una amplia gama de comportamientos celulares que tan solo al recibir más datos, un hallazgo que desafía una suposición común en el campo.
«Hay una tentación real de pensar que solo ampliar los conjuntos de datos resolverá estos problemas», dice Peter Winter, codirector del Proyecto Ex Vivo e investigador principal en el Broad Institute, una institución independiente sin ánimo de lucro con estrechos vínculos con el MIT y la Universidad de Harvard. «Pero la diversidad de estados celulares en esos conjuntos de datos moldea de manera fundamental qué tipo de conocimientos pueden producir estos modelos.»
El siguiente paso es definir con claridad los estados celulares y validarlos a través de los cánceres, con el objetivo de proporcionar a los médicos mejor información para ayudar a guiar las decisiones de tratamiento.
«Hay un mundo en el que dentro de cinco años este espacio se ve muy diferente a como es ahora, lo cual me parece muy, muy alentador», dice Crawford. «La realidad de esto no está tan lejos.»
Imagen principal: Foto de Gregory Winter
Susanna Ray escribe sobre IA y tecnología, con relatos que muestran su impacto en el mundo real y examinan cómo la innovación está transformando el trabajo, los negocios y la sociedad. Antes reportó para Bloomberg News y otras grandes organizaciones internacionales de noticias en EE. UU. y en el extranjero, donde cubría temas que iban desde política y gobierno hasta negocios y aviación. Sigan su trabajo en Microsoft Source.
AWS Graviton5-based M9g database (DB) instances are now generally available for Amazon Relational Database Service (RDS) for PostgreSQL, MySQL, and MariaDB. Graviton5-based instances provide up to a 30% performance improvement and up to a 23% price/performance improvement for on-demand pricing over Graviton4-based instances of equivalent sizes on Amazon RDS open source databases, depending on database engine, version, and workload.
AWS Graviton5 processors are the latest generation of custom-designed AWS Graviton processors built on the AWS Nitro System. M9g DB instances are available with new 24xlarge and 48xlarge sizes. With these new sizes, M9g DB instances offer up to 192 vCPU, up to 100Gbps enhanced networking bandwidth, and up to 72Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS).
These instances are now available in the US East (N. Virginia, Ohio), US West (Oregon), and Europe (Frankfurt) Regions. For complete information on pricing and regional availability, please refer to the Amazon RDS pricing page. For information on specific engine versions that support these DB instance types, please see the Amazon RDS documentation.
AWS Graviton5-based M9g database (DB) instances are now generally available for Amazon Relational Database Service (RDS) for PostgreSQL, MySQL, and MariaDB. Graviton5-based instances provide up to a 30% performance improvement and up to a 23% price/performance improvement for on-demand pricing over Graviton4-based instances of equivalent sizes on Amazon RDS open source databases, depending on database engine, version, and workload. AWS Graviton5 processors are the latest generation of custom-designed AWS Graviton processors built on the AWS Nitro System. M9g DB instances are available with new 24xlarge and 48xlarge sizes. With these new sizes, M9g DB instances offer up to 192 vCPU, up to 100Gbps enhanced networking bandwidth, and up to 72Gbps of bandwidth to the Amazon Elastic Block Store (Amazon EBS). These instances are now available in the US East (N. Virginia, Ohio), US West (Oregon), and Europe (Frankfurt) Regions. For complete information on pricing and regional availability, please refer to the Amazon RDS pricing page. For information on specific engine versions that support these DB instance types, please see the Amazon RDS documentation.
AWS Glue Interactive Sessions now support Apache Spark Connect, using which you can now develop and run Apache Spark applications from your preferred environment, including managed notebooks in Amazon SageMaker Unified Studio, or your preferred notebook environments and IDEs like Jupyter, Visual Studio Code, while running them on AWS Glue’s serverless infrastructure without managing clusters.
With Spark Connect, you submit Spark jobs to AWS Glue Interactive Sessions using a thin client architecture that decouples your client application from the Spark execution environment. This unlocks workflows like ad hoc data exploration, iterative step-by-step debugging, and incremental PySpark job development before deploying to production, all from the tools you already use. Spark Connect also simplifies upgrades and improves stability by isolating client dependencies from the server-side Spark runtime. For observability, you get real-time session monitoring via the Spark UI, history tracking through the Spark History Server, and session management using the AWS Glue API, CLI, or SDK.
AWS Glue Interactive Sessions with Spark Connect is available in Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, London, Paris, Stockholm), South America (São Paulo), US East (Ohio, N. Virginia), and US West (Oregon).
AWS Glue Interactive Sessions now support Apache Spark Connect, using which you can now develop and run Apache Spark applications from your preferred environment, including managed notebooks in Amazon SageMaker Unified Studio, or your preferred notebook environments and IDEs like Jupyter, Visual Studio Code, while running them on AWS Glue’s serverless infrastructure without managing clusters. With Spark Connect, you submit Spark jobs to AWS Glue Interactive Sessions using a thin client architecture that decouples your client application from the Spark execution environment. This unlocks workflows like ad hoc data exploration, iterative step-by-step debugging, and incremental PySpark job development before deploying to production, all from the tools you already use. Spark Connect also simplifies upgrades and improves stability by isolating client dependencies from the server-side Spark runtime. For observability, you get real-time session monitoring via the Spark UI, history tracking through the Spark History Server, and session management using the AWS Glue API, CLI, or SDK. AWS Glue Interactive Sessions with Spark Connect is available in Asia Pacific (Mumbai, Seoul, Singapore, Sydney, Tokyo), Canada (Central), Europe (Frankfurt, Ireland, London, Paris, Stockholm), South America (São Paulo), US East (Ohio, N. Virginia), and US West (Oregon). To get started, connect to Glue Interactive Sessions using Spark Connect from notebooks in Amazon SageMaker Unified Studio, your favorite IDE with a Python interpreter, or the AWS API, SDK, and CLI. To learn more, visit the AWS Glue Interactive Sessions documentation.
Amazon Aurora MySQL-Compatible Edition and Amazon Relational Database Service (RDS) for MySQL now offer Amazon RDS Extended Support for MySQL 5.7 through June 30, 2029, from the previous end date of February 28, 2027. This applies to Aurora MySQL version 2 (with MySQL 5.7 compatibility) and RDS for MySQL version 5.7, giving customers additional time to plan and complete their upgrades to a supported major version while continuing to receive critical security patches and bug fixes.
RDS Extended Support delivers security patches for critical and high CVEs, bug fixes for critical operational issues, and access to AWS Support within the standard Aurora and RDS SLAs. There is no price increase with this extension, and customers using RDS Extended Support for MySQL 5.7 will continue to pay Year 3 pricing through June 30, 2029. For pricing details, see Aurora pricing and RDS for MySQL pricing.
We recommend upgrading to MySQL 8.0 or MySQL 8.4 compatible versions to benefit from the latest database features, performance improvements, and security enhancements. You can upgrade using Amazon RDS Blue/Green Deployments, in-place upgrade, or snapshot restore. To learn more, see the Aurora MySQL and RDS for MySQL user guides. This extension is available in all AWS Regions where Aurora MySQL and RDS for MySQL are available.
Amazon Aurora is designed for high performance and availability at global scale with full MySQL and PostgreSQL compatibility. Amazon RDS for MySQL, PostgreSQL, and MariaDB make it simple to set up, operate, and scale open source deployments in the cloud. Visit the getting started pages for Aurora and RDS to begin.
Amazon Aurora MySQL-Compatible Edition and Amazon Relational Database Service (RDS) for MySQL now offer Amazon RDS Extended Support for MySQL 5.7 through June 30, 2029, from the previous end date of February 28, 2027. This applies to Aurora MySQL version 2 (with MySQL 5.7 compatibility) and RDS for MySQL version 5.7, giving customers additional time to plan and complete their upgrades to a supported major version while continuing to receive critical security patches and bug fixes. RDS Extended Support delivers security patches for critical and high CVEs, bug fixes for critical operational issues, and access to AWS Support within the standard Aurora and RDS SLAs. There is no price increase with this extension, and customers using RDS Extended Support for MySQL 5.7 will continue to pay Year 3 pricing through June 30, 2029. For pricing details, see Aurora pricing and RDS for MySQL pricing. We recommend upgrading to MySQL 8.0 or MySQL 8.4 compatible versions to benefit from the latest database features, performance improvements, and security enhancements. You can upgrade using Amazon RDS Blue/Green Deployments, in-place upgrade, or snapshot restore. To learn more, see the Aurora MySQL and RDS for MySQL user guides. This extension is available in all AWS Regions where Aurora MySQL and RDS for MySQL are available. Amazon Aurora is designed for high performance and availability at global scale with full MySQL and PostgreSQL compatibility. Amazon RDS for MySQL, PostgreSQL, and MariaDB make it simple to set up, operate, and scale open source deployments in the cloud. Visit the getting started pages for Aurora and RDS to begin.
AWS announces the availability of bmn-cx3a instances on second-generation AWS Outposts racks. Bmn-cx3a instances feature 5th Gen AMD EPYC processors with a maximum frequency of 4.1 GHz and NVIDIA ConnectX-7 (CX7) network interface cards, delivering up to 800 Gbps of bare-metal accelerated network bandwidth operating at near line rate.
Bmn-cx3a instances offer up to 256 cores and 1.5 TB of memory across two sizes, bmn-cx3a.metal-32xl and bmn-cx3a.metal-64xl, with 2x 8 TB NVMe SSD storage. With native Layer 2 (L2) multicast and hardware Precision Time Protocol (PTP) support, bmn-cx3a instances are designed for high-throughput workloads such as real-time market data ingestion and distribution, market and risk analytics, telecom 5G core network applications, and media distribution.
Bmn-cx3a instances on AWS Outposts racks are available in all countries and regions where second-generation Outposts racks are supported. For a current list of AWS Regions and countries/territories where Outposts racks are supported, check out the Outposts rack FAQs page.
AWS announces the availability of bmn-cx3a instances on second-generation AWS Outposts racks. Bmn-cx3a instances feature 5th Gen AMD EPYC processors with a maximum frequency of 4.1 GHz and NVIDIA ConnectX-7 (CX7) network interface cards, delivering up to 800 Gbps of bare-metal accelerated network bandwidth operating at near line rate. Bmn-cx3a instances offer up to 256 cores and 1.5 TB of memory across two sizes, bmn-cx3a.metal-32xl and bmn-cx3a.metal-64xl, with 2x 8 TB NVMe SSD storage. With native Layer 2 (L2) multicast and hardware Precision Time Protocol (PTP) support, bmn-cx3a instances are designed for high-throughput workloads such as real-time market data ingestion and distribution, market and risk analytics, telecom 5G core network applications, and media distribution. Bmn-cx3a instances on AWS Outposts racks are available in all countries and regions where second-generation Outposts racks are supported. For a current list of AWS Regions and countries/territories where Outposts racks are supported, check out the Outposts rack FAQs page.
