内容来源：https://techcrunch.com/2026/03/17/mistral-forge-nvidia-gtc-build-your-own-ai-enterprise/

内容总结：

法国AI创企Mistral推出企业定制平台，瞄准大模型落地痛点

在近日举行的英伟达GTC大会上，法国人工智能初创公司Mistral宣布推出全新平台“Mistral Forge”，旨在帮助企业利用自身数据从头训练定制化AI模型，以解决当前企业AI项目普遍面临的“模型不懂业务”的核心困境。

当前，许多企业AI项目失败的主要原因并非技术缺失，而是通用大模型往往基于互联网公开数据训练，缺乏对企业数十年积累的内部文档、工作流程与专业知识的具体理解。Mistral此举正是瞄准了这一市场缺口。

与行业内常见的微调现有模型或采用检索增强生成（RAG）等技术不同，Mistral Forge宣称允许企业从零开始训练专属模型。公司产品负责人埃莉萨·萨拉曼卡表示，该平台能让企业和政府根据其特定需求定制AI模型。这种深度定制方式理论上能更好地处理非英语或高度专业化的领域数据，赋予企业对模型行为更强的控制力，并通过强化学习训练智能体系统，降低对第三方模型供应商的依赖及相关风险。

Mistral联合创始人兼首席技术官蒂莫泰·拉克鲁瓦指出，平台允许客户利用Mistral丰富的开源模型库（包括新近发布的Mistral Small 4等轻量模型）进行构建，并通过定制化来强化模型在特定领域的表现。客户可自主决定模型与基础设施的选用，同时，Mistral还提供一支前沿部署工程师团队，深入客户内部协助数据梳理与需求对接，这一服务模式借鉴了IBM等公司的经验。

据Mistral透露，该平台已面向包括爱立信、欧洲航天局、意大利咨询公司Reply以及新加坡国防科技机构在内的早期合作伙伴开放。另一重要早期用户是去年9月以约117亿欧元估值领投Mistral C轮融资的荷兰芯片设备巨头ASML。这些合作案例体现了平台预期的核心应用场景：需适配语言文化的政府机构、高合规要求的金融企业、有定制化需求的制造商以及需让模型理解自身代码库的科技公司。

Mistral首席执行官亚瑟·门施强调，公司对企业市场的专注已见成效，预计今年年度经常性收入将突破10亿美元。在OpenAI与Anthropic等竞争对手于消费级市场高歌猛进之际，Mistral正通过赋予企业对其数据与AI系统的更高控制权，巩固其在企业级AI赛道的差异化定位。

中文翻译：

大多数企业人工智能项目失败，并非因为公司缺乏技术，而是由于他们使用的模型不了解自身业务。这些模型通常基于互联网数据进行训练，而非基于企业数十年的内部文档、工作流程和机构知识。

法国人工智能初创公司Mistral正是从中看到了机遇。4月2日，该公司发布了Mistral Forge平台，允许企业利用自有数据构建定制化模型。Mistral在英伟达年度技术大会GTC上宣布了这一平台，本届大会重点聚焦企业级人工智能与智能体模型。

这对Mistral而言是一次精准的战略布局。当竞争对手OpenAI和Anthropic在消费级市场高歌猛进时，Mistral始终将企业客户作为业务基石。首席执行官阿尔蒂尔·芒什表示，公司对企业市场的专注已见成效：预计今年年度经常性收入将突破10亿美元大关。

Mistral强调，深化企业战略的核心在于赋予企业对数据和人工智能系统的更强控制力。该公司产品负责人埃莉萨·萨拉曼卡向TechCrunch透露："Forge的核心理念是让企业和政府能够根据特定需求定制人工智能模型。"

尽管多家企业人工智能服务商宣称提供类似功能，但多数仅专注于微调现有模型，或通过检索增强生成等技术在表层叠加专有数据。这类方法并未对模型进行根本性重训练，而是在运行时利用企业数据进行适配或查询。

相比之下，Mistral宣称能让企业实现从零开始训练模型。理论上，这种方法能突破常见方案的局限：例如更好地处理非英语或高度专业化的领域数据，并增强对模型行为的控制力。企业还可借此通过强化学习训练智能体系统，降低对第三方模型供应商的依赖，规避模型变更或停用等风险。

Forge客户可选用Mistral丰富的开源权重模型库构建定制模型，其中包含新近推出的轻量级模型Mistral Small 4。联合创始人兼首席技术官蒂莫泰·拉克鲁瓦指出，Forge能帮助客户从现有模型中挖掘更大价值："开发轻量模型时我们需做出权衡——它们无法像大型模型那样全面覆盖所有主题。定制化功能让我们能自主选择强化或弱化特定能力。"

拉克鲁瓦表示，Mistral会提供模型与基础设施的选用建议，但最终决策权归属客户。对于需要深度支持的团队，Forge还配备了前线部署工程师团队。这些工程师将驻场服务，帮助客户梳理数据体系并适配需求——这种模式借鉴了IBM和Palantir等公司的成熟经验。

萨拉曼卡补充道："作为标准化产品，Forge已集成全套工具链和基础设施，可支持合成数据管道生成。但企业通常缺乏构建评估体系与确定数据规模的专业能力，这正是前线部署工程师团队的核心价值。"

目前Mistral已向爱立信、欧洲航天局、意大利咨询公司Reply以及新加坡国防科技局和HTX等合作伙伴开放Forge平台。早期采用者还包括荷兰芯片制造商ASML——该公司去年9月领投了Mistral的C轮融资，当时估值达117亿欧元（约合138亿美元）。

这些合作案例预示着Forge的主要应用场景。据首席营收官玛乔丽·雅涅维奇介绍，目标客户包括：需要适配本土语言文化的政府机构、合规要求严苛的金融机构、存在定制化需求的制造企业，以及需将模型适配自有代码库的科技公司。

英文来源：

Most enterprise AI projects fail not because companies lack the technology, but because the models they’re using don’t understand their business. The models are often trained on the internet, rather than decades of internal documents, workflows, and institutional knowledge.
That gap is where Mistral, the French AI startup, sees opportunity. On Tuesday, the company announced Mistral Forge, a platform that lets enterprises build custom models trained on their own data. Mistral announced the platform at Nvidia GTC, Nvidia’s annual technology conference, which this year is focused heavily on AI and agentic models for enterprise.
It’s a pointed move for Mistral, a company that has built its business on corporate clients while rivals OpenAI and Anthropic have soared ahead in terms of consumer adoption. CEO Arthur Mensch says Mistral’s laser focus on the enterprise is working: The company is on track to surpass $1 billion in annual recurring revenue this year.
A big part of doubling down on enterprise is giving companies more control over their data and their AI systems, Mistral says.
“What Forge does is it lets enterprises and governments customize AI models for their specific needs,” Elisa Salamanca, Mistral’s head of product, told TechCrunch.
Several companies in the enterprise AI space already claim to offer similar capabilities, but most focus on fine-tuning existing models or layering proprietary data on top through techniques like retrieval augmented generation (RAG). These approaches don’t fundamentally retrain models; instead, they adapt or query them at runtime using company data.
Mistral, by contrast, says it is enabling companies to train models from scratch. In theory, this could address some of the limitations of more common approaches — for example, better handling of non-English or highly domain-specific data, and greater control over model behavior. It could also allow companies to train agentic systems using reinforcement learning and reduce reliance on third-party model providers, avoiding risks like model changes or deprecation.
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Forge customers can build their custom models using Mistral’s wide library of open-weight AI models, which includes small models such as the recently introduced Mistral Small 4. According to Mistral co-founder and chief technologist, Timothée Lacroix, Forge can help unlock more value out of its existing models.
“The trade-offs that we make when we build smaller models is that they just cannot be as good on every topic as their larger counterparts, and so the ability to customize them lets us pick what we emphasize and what we drop,” Lacroix said.
Mistral advises on which models and infrastructure to use, but both decisions stay with the customer, Lacroix said. And for teams that need more than guidance, Forge comes with Mistral’s team of forward-deployed engineers who embed directly with customers to surface the right data and adapt to their needs — a model borrowed from the likes of IBM and Palantir.
“As a product, Forge already comes with all the tooling and infrastructure so you can generate synthetic data pipelines,” Salamanca said. “But understanding how to build the right evals and making sure that you have the right amount of data is something that enterprises usually don’t have the right expertise for, and that’s what the FDEs bring to the table.”
Mistral has already made Forge available to partners, including Ericsson, the European Space Agency, Italian consulting company Reply, and Singapore’s DSO and HTX. Early adopters also include ASML, the Dutch chipmaker that led Mistral’s Series C round last September at a €11.7 billion valuation (approximately $13.8 billion at the time).
These partnerships are emblematic of what Mistral expects Forge’s main use cases to be. According to Mistral’s chief revenue officer Marjorie Janiewicz, these include governments who need to tailor models for their language and culture; financial players with high compliance requirements; manufacturers with customization needs; and tech companies that need to tune models to their code base.