思考
目录
Mistral与NVIDIA合作，通过将Mistral的AI模型与NVIDIA的加速计算和物理建模工具相结合，实现工程工作流程的革命化，使得更快的模拟和闭路式AI驱动设计成为可能。 该伙伴关系将引入公共的基准测试和开放数据集，使计算机辅助工程中物理和代理AI的评价标准化，同时利用NVIDIA的Omniverse进行3D模拟和资产创造。 在其现有工作，包括开发Mistral Large 4的基础上，该倡议旨在推动AI动力工程跨越航空航天、汽车和半导体等行业的界限。
Mistral正与NVIDIA合作开发由NVIDIA加速计算驱动的新一代AI-内在工程工作流程。 该倡议将Mistral大4与Mistral的数据生成，以及由NVIDIA CUDA-X库和NVIDIA Warp加速的物理建模管道相结合，旨在加速工程模拟，并启用连接模拟，AI模型开发和工程设计的闭路工作流程。 Mistral和NVIDIA还将合作将Mistral的模型与NVIDIA Omniverse库集成，用于GPU加速的3D场景生成和SimReady资产创建。
合作的中心焦点是开发用于评价物理模型的公共基准测试和开放数据集。 这些基准测试将为研究人员，开发者和工业团队提供一个共享的基础，以比较模型的准确性和性能，并构建和验证自动CAD到模拟工作流程。
"太长时间以来，工程进度都由模拟速度决定。 Mistral的开量模型和物理AI能力，加上NVIDIA的加速计算堆栈，我们正在给予工程师更快的模拟周期，以及足够严谨的基准测试在真正的CAE和EDA工作上验证agents". Neil Ashton，计算工程公司副总裁，Mistral
除了物理AI之外，Mistral和NVIDIA还将合作开发计算机辅助工程(CAE)和电子设计自动化(EDA)的特异性AI的专用评价和基准测试，以加速AI在航空航天，汽车和半导体使用箱内的工程应用。 这些基准测试将评估agents使用工程工具，完成多步工作流程和产生技术上正确结果的能力，同时测量执行时间和计算成本。
通过这项工作，Mistral和NVIDIA将帮助确定工程中代理人和物理AI评价的标准，建立一致，可复制的方法来评估能力和跟踪进度。 在NVIDIA加速计算，库和工具的动力下，Mistral将重新定义工程的AI，改造工程师如何设计，模拟和建造下一代产品。
称 Tim Costa是NVIDIA工业和计算工程的副总裁和总经理。 Mistral的高级开放模型和物理学AI的深度专业知识与NVIDIA的加速计算和图书馆相结合，我们帮助工程师推开设计界限——与共享产业基准测试建立对AI能做什么的信心。
以正在进行的NVIDIA和Mistral伙伴关系为基础
工业工程合作建立在NVIDIA和Mistral之间持续的伙伴关系之上，这种伙伴关系涉及开放模型开发、特定领域的AI以及将这些能力投入生产的基础设施。
之前共同开发的Mistral NeMo公司，现在通过NVIDIA Nemotron联盟合作开发一个前沿基地模型，该模型将继续推进开放重量前沿。 这一共享的基础将使世界各地的开发者和企业能够在联盟成员提供的数据、评价和领域专门知识的支持下，为其行业、语言和区域提供专门的AI。
Mistral最近推出是Mistral Large 4的公开预览，被称为Le Chonk，是其迄今为止最大，最有能力的前沿模型。 利用约1万亿总参数和490亿有效参数，该多式联运模型在欧洲Mistral运营的数据中心接受了3800 NVIDIA Grace Blackwell GPU的培训。 它跨越编码、代理工作流程和多式联运理解，提高能力，重量计划于10月底发布。
除了模型开发外，Mistral和NVIDIA正在扩大授权AI培训，定制和推论的基础设施。 Mistral将新增NVIDIA动力AI基础设施上线，支持Mistral的计算路线图，目标是到2030年实现1GW容量。
这种扩张加强了欧洲在为世界发展和提供AI方面的作用。 Mistral和NVIDIA通过将欧洲基础设施和研究与开放型号，专业工程能力和客户应用联系起来，帮助企业围绕自身专长建立AI，并扩大其规模，为全球市场服务。
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Thinking
Summary
Mistral and NVIDIA are collaborating to revolutionize engineering workflows by integrating Mistral’s AI models with NVIDIA’s accelerated computing and physics modeling tools, enabling faster simulations and closed-loop AI-driven design. The partnership will introduce public benchmarks and open datasets to standardize evaluations for physics and agentic AI in computer-aided engineering, while leveraging NVIDIA’s Omniverse for 3D simulation and asset creation. Building on their existing work, including the development of Mistral Large 4, the initiative aims to push the boundaries of AI-powered engineering across industries like aerospace, automotive, and semiconductors.
Mistral is collaborating with NVIDIA to develop a new generation of AI-native engineering workflows powered by NVIDIA accelerated computing. Combining Mistral Large 4, with Mistral’s data generation, and physics modeling pipelines, accelerated by NVIDIA CUDA-X libraries and NVIDIA Warp, the initiative aims to accelerate engineering simulation and enable closed-loop workflows that connect simulation, AI model development and engineering design. Mistral and NVIDIA will also work together to integrate Mistral's models with NVIDIA Omniverse libraries for GPU-accelerated 3D scene generation and SimReady asset creation.
A central focus of the collaboration is the development of public benchmarks and open datasets for evaluating physics models. These benchmarks will give researchers, developers and industrial teams a shared foundation to compare model accuracy and performance and build and validate automated CAD-to-simulation workflows.
"For too long, the pace of engineering has been set by the pace of simulation. With Mistral's open-weight models and physics AI capabilities, combined with NVIDIA’s accelerated computing stack, we’re giving engineers faster simulation cycles, as well as open benchmarks rigorous enough to validate agents on real CAE and EDA work.” Neil Ashton, VP of Computational Engineering, Mistral
Beyond physics AI, Mistral and NVIDIA will also work together to develop dedicated evaluations and benchmarks for agentic AI in computer-aided engineering (CAE) and electronic design automation (EDA) to accelerate the application of AI for engineering within aerospace, automotive and semiconductor use-cases. These benchmarks will assess agents’ ability to use engineering tools, complete multistep workflows and produce technically correct results, while measuring execution time and computational cost.
Through this work, Mistral and NVIDIA will help define standards for agentic and physics AI evaluations in engineering, establishing consistent, reproducible methods for assessing capabilities and tracking progress. Powered by NVIDIA accelerated computing, libraries and tools, Mistral will redefine AI for engineering, transforming how engineers design, simulate and build the next generation of products.
“The next wave of engineering breakthroughs will come from exploring ideas that were once too complex or time-consuming to test,” said Tim Costa, vice president and general manager of industrial and computational engineering at NVIDIA. “By combining Mistral’s advanced open models and deep expertise in physics AI with NVIDIA accelerated computing and libraries, we’re helping engineers push the boundaries of design—with shared industry benchmarks to build confidence in what AI can do.”
Building on the ongoing NVIDIA and Mistral partnership
The industrial engineering collaboration builds on an ongoing partnership between NVIDIA and Mistral spanning open model development, domain-specific AI and the infrastructure needed to bring these capabilities into production.
The companies previously co-developed Mistral NeMo and are now working together through the NVIDIA Nemotron Coalition to develop a frontier base model that will continue to advance the open weight frontier. This shared foundation will enable developers and enterprises worldwide to specialize AI for their industries, languages and regions, supported by contributions of data, evaluations and domain expertise from coalition members.
Mistral recently introduced the public preview of Mistral Large 4, known as Le Chonk, its largest and most capable frontier model to date. With approximately 1 trillion total parameters and 49 billion active parameters, the multimodal model was trained on 3,800 NVIDIA Grace Blackwell GPUs in Mistral-operated data centers in Europe. It advances capabilities across coding, agentic workflows and multimodal understanding, with weights planned for release at the end of October.
Alongside model development, Mistral and NVIDIA are expanding the infrastructure that powers AI training, customization and inference. Mistral is bringing additional NVIDIA-powered AI infrastructure online in support of Mistral’s compute roadmap targeting 1 GW of capacity by 2030.
This expansion strengthens Europe’s role in developing and delivering AI for the world. By connecting European infrastructure and research with open models, specialized engineering capabilities and customer applications, Mistral and NVIDIA are helping enterprises build AI around their own expertise and scale it to serve markets worldwide.
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