---
格式版本: 2
标题: "NVIDIA Unlocks AI Compute at Scale, Inviting Partners to Power the AI Infrastructure Buildout | NVIDIA Blog"
原文链接: "https://blogs.nvidia.com/blog/nvidia-unlocks-ai-compute-at-scale-capital-partners-to-power-ai-infrastructure-buildout/"
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: "该日期来自HTML中的time标签，class包含nvidia-article-date，且位于标题附近，符合文章发布时间的特征。"
发布时间校准置信度: "1"
发布时间候选数量: 36
发布时间严格候选数量: 12
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: "2026-07-20T09:33:51+08:00"
发现时间: "2026-07-20T09:23:38+08:00"
入库时间: "2026-07-20T01:41:00.718Z"
来源平台: "NVIDIA Blog 搜索"
搜索渠道: "source_template"
搜索词: "https://blogs.nvidia.com/?s=AI%20Rack"
匹配关键词:
  - "AI Rack"
  - "GPU"
相关厂家:
  - "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: 68
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，主题涉及AI基础设施与GB300 GPU部署，但正文侧重商业模式与资本合作，缺乏超节点/AI Rack架构、供电散热互连等技术细节，技术深度不足。"
AI质检模型: "qwen3.6-plus"
AI质检时间: "2026-07-20T09:41:00+08:00"
AI主题相关性: 12
AI来源权威性: 14
AI新颖性: 16
AI技术细节: 6
AI商业部署信号: 10
AI完整性: 10
图片摘要:
  - "✓ ./assets/img-83d2f103.jpg | diagram | 展示AI工厂基础设施的3D渲染图，呈现大规模服务器集群布局，呼应正文算力规模化部署主题。"
  - "✗ ./assets/img-1f6575f1.png | diagram | 正文摘要未提及Vera CPU及单线程性能，主题不符。"
  - "✗ ./assets/img-3511966c.jpg | photo | 仅为NVIDIA品牌标识与建筑实拍，属装饰性配图。"
  - "✓ ./assets/img-e47e99dc.png | infographic | 展示NVIDIA推理软件栈概念图，强调降低Token成本，对应正文提到的推理服务经济性。"
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/nvidia-unlocks-ai-compute-at-scale-capital-partners-to-power-ai-infrastructure-buildout"
---

As AI moves from model development to production inference, compute demand is accelerating and shifting toward continuously operating AI factories that generate tokens at scale. This shift requires access to large‑scale, multi‑tenant accelerated computing that can come online quickly, stay highly utilized and support the economics of token‑scale AI services.

Emerging AI companies historically have had limited access to capital-intensive infrastructure, with even long-term commitments insufficient to unlock financing for compute.

To address this, NVIDIA is introducing a new business model that opens up compute access to the fast‑growing AI ecosystem of startups, model builders, enterprises, research organizations and regional AI players.

This new model enables AI clouds to procure NVIDIA infrastructure for AI-native, enterprise and ISV customers through economic alignment with a revenue-sharing and credit-support model. Through the partnership, AI clouds will sell NVIDIA-powered cloud services, with NVIDIA earning both standard product revenue and a share of the cloud revenue on the supported capacity. This structure accelerates adoption of NVIDIA platforms among the high-growth, high-conviction AI native sector, and provides NVIDIA with a recurring, usage-linked earnings stream.

For model builders, inference providers, agent platforms and enterprises scaling AI, it can mean faster access to full-stack accelerated computing without waiting through site selection, power procurement, construction and hardware bring-up.

## NVIDIA AI Factory Capacity Built Around Demand

The initiative is already taking shape, with AI cloud companies building DSX AI factories designed to serve customers and workloads across regions.

Sharon AI and Firmus are among the first companies to work with NVIDIA on this new business model.

Sharon AI is deploying up to 40,000 NVIDIA Grace Blackwell GB300 GPUs.

“This strategic collaboration with NVIDIA marks a pivotal moment in Sharon AI’s mission to deliver sovereign, large-scale AI compute infrastructure,” said James Manning, cofounder and CEO of Sharon AI.

Firmus is building a DSX AI factory campus in Batam, Indonesia. The campus is expected to scale to 360 megawatts and up to 170,000 NVIDIA GPUs.

“AI-native companies need access to scalable, energy- and cost-efficient compute infrastructure to compete globally,” said Tim Rosenfield, co-CEO of Firmus Technologies. “Firmus AI cloud is building a NVIDIA DSX-aligned AI factory, which will enable our cloud to help more customers access the compute they need to build and scale AI.”

AI natives such as Baseten, Fireworks AI and Together AI show where compute demand is headed: they need immediate access to AI cloud capacity to run model training, post-training, fine-tuning and high-volume agentic inference for developers, digital natives and enterprises building with AI.

Their customers need reliable access to large-scale NVIDIA accelerated computing as usage grows, but they also need commercial flexibility as products move from pilot to production.

*To secure compute capacity and build and deploy AI models, contact Sharon AI and Firmus.*

*Learn more about* [*NVIDIA Cloud Partners*](https://www.nvidia.com/en-us/data-center/gpu-cloud-computing/partners/) *and* [*AI factories*](https://www.nvidia.com/en-us/glossary/ai-factory/)*.*

![Why Performance per Watt Is the Ultimate Metric for AI Infrastructure Efficiency](./assets/img-83d2f103.jpg)

![How NVIDIA’s Inference Software Stack Powers the Lowest Token Cost](./assets/img-e47e99dc.png)