--- 格式版本: 2 标题: "Latest NVIDIA AI, Robotics and Quantum Computing Software Comes to AWS | NVIDIA Blog" 原文链接: "https://blogs.nvidia.com/blog/aws-reinvent-ai-robotics-quantum-software/" 发布日期: "2026-06-30" 发布时间校准状态: "found" 发布时间来源: "llm:strict_original_body" 发布时间证据: "time class=related-news-date nvidia-article-date datetime=2026-06-30T08:00:57-07:00: Jun 30, 2026" 发布时间校准原因: "该日期位于标题下方，且带有 nvidia-article-date 类名，符合文章发布时间的特征。" 发布时间校准置信度: "100" 发布时间候选数量: 34 发布时间严格候选数量: 8 发布时间原页读取状态: "原页面来自已抓取 HTML" 发布时间未找到原因: "候选日期无效或 LLM 未确认" 发布时间校准时间: "2026-07-20T12:34:25+08:00" 发现时间: "2026-07-20T09:26:26+08:00" 入库时间: "2026-07-20T04:43:08.407Z" 来源平台: "NVIDIA Blog 搜索" 搜索渠道: "source_template" 搜索词: "https://blogs.nvidia.com/?s=AWS" 匹配关键词: - "GPU" - "Liquid Cooling" - "NVL72" 相关厂家: - "AWS" - "NVIDIA" 相关专家: [] 内容类型: "网页" 抓取工具: "AgentKey Scrape" 清洗工具: "AgentKey Markdown + LLM 正文裁剪" 原始附件: [] AI优质: "否" AI打分: 62 AI分档: "召回候选" AI质检状态: "不通过" AI打分理由: "NVIDIA官方发布，提及AWS液冷数据中心与GB200 NVL72集成，但正文以软件/云服务/生态合作为主，缺乏机柜级架构、供电/散热/互连等硬件技术细节，且发布日期为2026-06-30疑似未来时间，整体偏商业与软件部署，硬件深度不足。" AI质检模型: "qwen3.6-plus" AI质检时间: "2026-07-20T12:43:08+08:00" AI主题相关性: 12 AI来源权威性: 14 AI新颖性: 14 AI技术细节: 8 AI商业部署信号: 10 AI完整性: 4 图片摘要: - "★ ./assets/img-83d2f103.jpg | diagram | 展示高密度AI服务器机架与液冷管路连接的数据中心内部架构，体现 rack-scale 超算系统的物理布局与互连。" - "★ ./assets/img-1f6575f1.png | diagram | 展示 GPU 与 Vera CPU 之间的互连架构及工作负载分配示意图，体现 CPU-GPU 协同拓扑。" - "✗ ./assets/img-3511966c.jpg | photo | 品牌宣传/装饰性配图（NVIDIA总部大楼），无实质技术信息" - "✓ ./assets/img-e47e99dc.png | infographic | 展示 NVIDIA 推理软件栈的概念图，以发光芯片和数据流象征 AI 推理算力与成本优化。" 采集批次: "2026年7月20日9点23分34秒" 采集批次ID: "20260720-092334-062" 去重键: "https://blogs.nvidia.com/blog/aws-reinvent-ai-robotics-quantum-software" --- Expanding what’s possible for developers and enterprises in the cloud, NVIDIA and Amazon Web Services are converging at AWS re:Invent in Las Vegas this week to showcase new solutions designed to accelerate AI and robotics breakthroughs and simplify research in quantum computing development. AWS re:Invent is a conference for the global cloud-computing community packed with keynotes and more than 2,000 technical sessions. Announcement highlights include the availability of [NVIDIA DGX Cloud](https://www.nvidia.com/en-us/data-center/dgx-cloud/) on AWS and enhanced AI, quantum computing and robotics tools. ## NVIDIA DGX Cloud on AWS for AI at Scale The [NVIDIA DGX Cloud](https://www.nvidia.com/en-us/data-center/dgx-cloud/) AI computing platform is now available through AWS Marketplace Private Offers, offering a high-performance, fully managed solution for enterprises to train and customize AI models. DGX Cloud offers flexible terms, a fully managed and optimized platform, and direct access to NVIDIA experts to help businesses scale their AI capabilities quickly. Early adopter Leonardo.ai, part of the Canva family, is already using DGX Cloud on AWS to develop advanced design tools. ## AWS Liquid-Cooled Data Centers With NVIDIA Blackwell Newer AI servers benefit from liquid cooling to cool high-density compute chips more efficiently for better performance and energy efficiency. AWS has developed solutions that provide configurable liquid-to-chip cooling across its data centers. The cooling solution announced today will seamlessly integrate air- and liquid-cooling capabilities for the most powerful rack-scale AI supercomputing systems like [NVIDIA GB200 NVL72](https://www.nvidia.com/en-us/data-center/gb200-nvl72/), as well as AWS’ network switches and storage servers. This flexible, multimodal cooling design provides maximum performance and efficiency for running AI models and will be used for the next-generation [NVIDIA Blackwell](https://www.nvidia.com/en-us/data-center/technologies/blackwell-architecture/) platform. Blackwell will be the foundation of Amazon EC2 P6 instances, DGX Cloud on AWS and [Project Ceiba](https://aws.amazon.com/nvidia/project-ceiba/). ## NVIDIA Advances Physical AI With Accelerated Robotics Simulation on AWS NVIDIA is also expanding the reach of [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/) on AWS with [NVIDIA Isaac Sim](https://developer.nvidia.com/isaac/sim), now running on high-performance Amazon EC2 G6e instances accelerated by [NVIDIA L40S GPUs](https://www.nvidia.com/en-us/data-center/l40s/). Available now, this reference application built on [NVIDIA Omniverse](https://www.nvidia.com/en-us/omniverse/) enables developers to simulate and test AI-driven robots in physically based virtual environments. One of the many workflows enabled by Isaac Sim is [synthetic data generation](https://www.nvidia.com/en-us/use-cases/synthetic-data/). This pipeline is now further accelerated with the infusion of OpenUSD NIM microservices, from scene creation to data augmentation. Robotics companies such as [Aescape](https://www.aescape.com/news/scaling-the-perfect-massage?x-craft-preview=ZE05LZsLLm&token=Wjz4EqXJT-Y8mqIEytAqOIYBC95ASUVB), [Cohesive Robotics,](https://www.cohesiverobotics.com/blog/cohesive-robotics-supercharges-high-mix-manufacturing-automation-with-nvidia-isaac-sim) [Cobot,](https://www.co.bot/news/cobot-develops-ai-driven-collaborative-robots-with-nvidia-isaac) Field AI, Standard Bots, Swiss Mile and Vention are using Isaac Sim to simulate and validate the performance of their robots prior to deployment. In addition, Rendered.ai, [SoftServe](https://www.softserveinc.com/en-us/blog/ai-powered-synthetic-data-for-robotics) and Tata Consultancy Services are using the synthetic data generation capabilities of [Omniverse Replicator](https://docs.omniverse.nvidia.com/extensions/latest/ext_replicator.html) and Isaac Sim to bootstrap perception AI models that power various robotics applications. ## NVIDIA BioNeMo on AWS for Advanced AI-Based Drug Discovery [NVIDIA BioNeMo NIM microservices and AI Blueprints](https://www.nvidia.com/en-us/clara/biopharma/), developed to advance drug discovery, are now [integrated into AWS HealthOmics](https://aws.amazon.com/blogs/industries/accelerating-drug-discovery-with-aws-healthomics-and-nvidia-blueprints/), a fully managed biological data compute and storage service designed to accelerate scientific breakthroughs in clinical diagnostics and drug discovery. This collaboration gives researchers access to AI models and scalable cloud infrastructure tailored to drug discovery workflows. Several biotech companies already use NVIDIA BioNeMo on AWS to drive their research and development pipelines. For example, [A-Alpha Bio](https://www.aalphabio.com/), a biotechnology company based in Seattle, recently published a [study in biorxiv](https://www.biorxiv.org/content/10.1101/2024.11.11.622872v1) describing a collaborative effort with NVIDIA and AWS to develop and deploy an antibody AI model called AlphaBind. Using [AlphaBind via the BioNeMo framework](https://developer.nvidia.com/blog/in-silico-antibody-development-with-alphabind-using-nvidia-bionemo-and-aws-healthomics/) on Amazon EC2 P5 instances equipped with [NVIDIA H100 Tensor Core GPUs](https://www.nvidia.com/en-us/data-center/h100/), A-Alpha Bio achieved a 12x increase in inference speed and processed over [108 million inference](https://aws.amazon.com/solutions/case-studies/a-alpha-bio-case-study/) calls in two months. Additionally, [SoftServe](https://www.softserveinc.com/en-us/news/drug-discovery-with-nvidia-at-aws-reinvent-2024) today launched Drug Discovery, its generative AI solution built with NVIDIA Blueprints, to enable computer-aided drug discovery and efficient drug development. This solution is set to deliver faster workflows and will soon be available in AWS Marketplace. ## Real-Time AI Blueprints: Ready-to-Deploy Options for Video, Cybersecurity and More NVIDIA’s latest AI Blueprints are available for instant deployment on AWS, making real-time applications like [vulnerability analysis for container security](https://build.nvidia.com/nvidia/vulnerability-analysis-for-container-security), and video search and summarization agents readily accessible. Developers can easily integrate these blueprints into existing workflows to speed deployments. Developers and enterprises can use the [NVIDIA AI Blueprint for video search and summarization](https://build.nvidia.com/nvidia/video-search-and-summarization) to build visual AI agents that can analyze real-time or archived videos to answer user questions, generate summaries and enable alerts for specific scenarios. AWS collaborated with NVIDIA to provide a reference architecture applying the NVIDIA AI Blueprint for vulnerability analysis to augment early security patching in continuous integration pipelines on AWS cloud-native services. ## NVIDIA CUDA-Q on Amazon Braket: Quantum Computing Made Practical [NVIDIA CUDA-Q](https://developer.nvidia.com/cuda-q) is now integrated with Amazon Braket to streamline quantum computing development. CUDA-Q users can use Amazon Braket’s quantum processors, while Braket users can tap CUDA-Q’s GPU-accelerated workflows for development and simulation. The CUDA-Q platform allows developers to build hybrid quantum-classical applications and run them on many different types of quantum processors, simulated and physical. Now preinstalled on [Amazon Braket](https://aws.amazon.com/braket/?nc2=type%5C_a), CUDA-Q provides a seamless development platform for hybrid quantum-classical applications, unlocking new potential in quantum research. ## Enterprise Platform Providers and Consulting Leaders Advance AI With NVIDIA on AWS Leading software platforms and global system integrators are helping enterprises rapidly scale generative AI applications built with NVIDIA AI on AWS to drive innovation across industries. Cloudera is using NVIDIA AI on AWS to enhance its new AI inference solution, helping Mercy Corps improve the precision and effectiveness of its aid distribution technology. [Cohesity](https://www.cohesity.com/blogs/how-cohesity-uses-nvidia-nemo-retriever-microservices-to-improve-rag-ai-retrieval-recall/) has integrated NVIDIA NeMo Retriever microservices in its generative AI-powered conversational search assistant, Cohesity Gaia, to improve the recall performance of [retrieval-augmented generation](https://blogs.nvidia.com/blog/what-is-retrieval-augmented-generation/). Cohesity customers running on AWS can take advantage of the NeMo Retriever integration within Gaia. [DataStax](https://www.datastax.com/press-release/wikimedia-deutschland-launches-ai-knowledge-project-in-collaboration-with-datastax-built-with-nvidia-ai) announced that Wikimedia Deutschland is applying the DataStax AI Platform to make Wikidata available to developers as an embedded vectorized database. The Datastax AI Platform is built with NVIDIA NeMo Retriever and NIM microservices, and available on AWS. Deloitte’s C-Suite AI now supports [NVIDIA AI Enterprise](https://www.nvidia.com/en-us/data-center/products/ai-enterprise/) software, including NVIDIA NIM microservices and [NVIDIA NeMo](https://www.nvidia.com/en-us/ai-data-science/products/nemo/) for CFO-specific use cases, including financial statement analysis, scenario modeling and market analysis. ## RAPIDS Quick Start Notebooks Now Available on Amazon EMR NVIDIA and AWS are also speeding data science and data analytics workloads with the [RAPIDS Accelerator for Apache Spark](https://www.nvidia.com/en-us/lp/deep-learning-ai/solutions/data-science/spark-accelerator/), [which](https://www.nvidia.com/en-us/lp/deep-learning-ai/solutions/data-science/spark-accelerator/) accelerates analytics and machine learning workloads with no code change and reduces data processing costs by up to 80%. Quick Start notebooks for [RAPIDS Accelerator for Apache Spark](https://www.nvidia.com/en-us/lp/deep-learning-ai/solutions/data-science/spark-accelerator/) are now [available on](https://github.com/NVIDIA/spark-rapids-examples/tree/main/tools/emr) [Amazon EMR](https://docs.aws.amazon.com/emr/latest/ReleaseGuide/emr-spark-rapids.html), Amazon EC2 and [Amazon EMR on EKS. These](https://aws.amazon.com/blogs/containers/run-spark-rapids-ml-workloads-with-gpus-on-amazon-emr-on-eks/) offer a simple way to qualify Spark jobs tuned to maximize the performance of RAPIDS on GPUs, all within AWS EMR. ## NVIDIA and AWS Power the Next Generation of Industrial Edge Systems The [NVIDIA IGX Orin](https://www.nvidia.com/en-us/edge-computing/products/igx/) and [Jetson Orin](https://www.nvidia.com/en-us/autonomous-machines/embedded-systems/jetson-orin/) platforms now integrate seamlessly with [AWS IoT Greengrass](https://aws.amazon.com/greengrass/?nc2=h%5C_ql%5C_prod%5C_it%5C_gg) to streamline the deployment and running of AI models at the edge and to efficiently manage fleets of connected devices at scale. This combination enhances scalability and simplifies the deployment process for industrial and robotics applications. Developers can now tap into NVIDIA’s advanced edge computing power with AWS’ purpose-built IoT services, creating a secure, scalable environment for autonomous machines and smart sensors. A guide for getting started, authored by AWS, is now available to support developers putting these capabilities to work. The integration underscores NVIDIA’s work in advancing enterprise-ready industrial edge systems to enable rapid, intelligent operations in real-world applications. ## NVIDIA NIM Microservices Now Available Through Amazon Bedrock Marketplace Finally, on Dec. 4, [AWS announced that NVIDIA NIM microservices](https://blogs.nvidia.com/blog/nim-microservices-aws-inference) are now available directly through the Amazon Bedrock Marketplace, giving developers streamlined access to NVIDIA-optimized inference. This expansion supports faster AI inference and reduced latency for AI agents, physical AI applications and more. NVIDIA NIM is part of the NVIDIA AI Enterprise software platform, which is available through the [AWS Marketplace](https://aws.amazon.com/marketplace/pp/prodview-ozgjkov6vq3l6). This update simplifies the deployment of NIM microservices, including NVIDIA Nemotron-4, at scale. These offerings can also be accessed via the AWS Marketplace and SageMaker Jumpstart. *Catch more of NVIDIA’s work at AWS: re:Invent 2024 through live demos, technical sessions and hands-on labs.* Editor’s note: This blog has been updated to reflect news announced Wednesday, Dec.4, that AWS will extend NVIDIA NIM microservices across key AWS AI services. *See* [*notice*](https://www.nvidia.com/en-us/about-nvidia/legal-info/) *regarding software product information.* ![Why Performance per Watt Is the Ultimate Metric for AI Infrastructure Efficiency](./assets/img-83d2f103.jpg) ![AI Innovators Adopt NVIDIA Vera — Why Max Single-Threaded CPU at Scale Matters](./assets/img-1f6575f1.png) ![How NVIDIA’s Inference Software Stack Powers the Lowest Token Cost](./assets/img-e47e99dc.png)