---
格式版本: 2
标题: "NVIDIA Unlocks AI Compute at Scale, Inviting Partners to Power the AI Infrastructure Buildout"
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: "2026-07-01"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "manual"
发布时间证据: "标题附近明确标注的发布时间（span class=entry-date）优先级高于article:published_time，且无其他冲突证据。"
发布时间校准原因: "标题附近明确标注的发布时间（span class=entry-date）优先级高于article:published_time，且无其他冲突证据。"
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发布时间候选数量: 0
发布时间严格候选数量: 0
发布时间原页读取状态: "manual"
发布时间未找到原因: ""
发布时间校准时间: "2026-08-14T09:21:13+08:00"
发布时间仲裁状态: "manual"
发布时间仲裁尝试次数: 0
发布时间仲裁耗时毫秒: 0
发现时间: "2026-08-13T18:24:33+08:00"
入库时间: "2026-08-13T10:33:35.166Z"
来源平台: "NVIDIA Newsroom 搜索"
搜索渠道: "source_template"
搜索词: "https://nvidianews.nvidia.com/search?q=AI&year=&c=21924"
匹配关键词:
  - "AI"
  - "GPU"
相关厂家:
  - "NVIDIA"
相关专家:
  []
内容类型: "网页"
抓取工具: "Free Fetch + Defuddle"
清洗工具: "Defuddle Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI质检状态: "评分失败"
AI评分尝试次数: 1
AI评分错误类型: "service_error"
AI评分错误: "LLM call failed; tried model chain: ali-deepseek-v4-flash -> tx-deepseek-v4-flash | Model ali-deepseek-v4-flash failed 500: {\"error\":{\"code\":\"\",\"message\":\"Database error, please contact the administrator (request id: 202608131033368373785048268d9d6TkbwaahN)\",\"type\":\"new_api_error\"}} | Model tx-deepseek-v4-flash failed 500: {\"error\":{\"code\":\"\",\"message\":\"Database error, please contact the administrator (request id: 202608131033375629852268268d9d6W2xt8wQX)\",\"type\":\"new_api_error\"}}"
AI评分开始时间: "2026-08-13T10:33:35.174Z"
AI评分结束时间: "2026-08-13T10:33:37.698Z"
采集批次: "2026年8月13日18点24分27秒"
采集批次ID: "20260813-182427-1564e572"
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/)*.*