---
格式版本: 2
标题: "AI Infrastructure: When to Choose Cloud GPUs vs. Private Data Center GPUs"
原文链接: "https://www.datacenterknowledge.com/data-center-chips/ai-infrastructure-when-to-choose-cloud-gpus-vs-private-data-center-gpus"
发布日期: "2026-08-11"
发布时间校准状态: "found"
发布时间需复核: "否"
发布时间来源: "llm:local:strict_original_body"
发布时间证据: "Aug 11, 2026 出现在标题附近正文中，且为 strict_original_body 类型，符合发布时间标注特征。"
发布时间校准原因: "正文标题附近明确标注 Aug 11, 2026，且多个 later 候选均指向此日期，无其他更早的明确发布标注。"
发布时间校准置信度: "1"
发布时间候选数量: 48
发布时间严格候选数量: 4
发布时间原页读取状态: "source template page reused from URL open"
发布时间未找到原因: ""
发布时间校准时间: "2026-08-12T04:06:21+08:00"
发布时间仲裁状态: "confirmed"
发布时间仲裁尝试次数: 3
发布时间仲裁耗时毫秒: 103183
发现时间: "2026-08-12T03:57:51+08:00"
入库时间: "2026-08-11T20:08:04.410Z"
来源平台: "Data Center Knowledge 搜索"
搜索渠道: "source_template"
搜索词: "https://www.datacenterknowledge.com/search?q=model%20fit"
匹配关键词:
  - "model fit"
  - "AI"
  - "GPU"
  - "deployment"
  - "performance"
  - "latency"
相关厂家:
  - "NVIDIA"
  - "AMD"
  - "Microsoft"
  - "AWS"
  - "Google"
相关专家:
  []
内容类型: "网页"
抓取工具: "Jina Reader"
清洗工具: "Jina Reader Markdown + Defuddle/Readability 正文提取"
原始附件:
  []
AI优质: "否"
AI打分: 20
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "正文仅为泛泛的GPU选择科普，无超节点/AI Rack等具体架构、技术细节或商业部署信息，属于低价值内容。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T04:08:52+08:00"
AI主题相关性: 5
AI来源权威性: 8
AI新颖性: 5
AI技术细节: 0
AI商业部署信号: 0
AI完整性: 2
采集批次: "2026年8月12日2点04分20秒"
采集批次ID: "20260812-020420-289"
去重键: "https://www.datacenterknowledge.com/data-center-chips/ai-infrastructure-when-to-choose-cloud-gpus-vs-private-data-center-gpus"
---

Title: AI Infrastructure: When to Choose Cloud GPUs vs. Private Data Center GPUs

URL Source: https://www.datacenterknowledge.com/data-center-chips/ai-infrastructure-when-to-choose-cloud-gpus-vs-private-data-center-gpus

Published Time: 2025-06-09T07:35:00.000Z

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# AI Infrastructure: When to Choose Cloud GPUs vs. Private Data Center GPUs

Navigating GPU decisions for AI workloads involves determining when cloud flexibility outweighs the control and cost benefits of private data centers.

[![Image 17: Picture of Christopher Tozzi](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/bltb80c8a754e05f0af/6613d88533fe26f3ee4e5204/christophertozzi_2.jpg?width=100&auto=webp&quality=80&disable=upscale)](https://www.datacenterknowledge.com/author/christopher-tozzi)

[Christopher Tozzi,](https://www.datacenterknowledge.com/author/christopher-tozzi)Technology Analyst
June 9, 2025

4 Min Read

![Image 18: Data center GPU technical illustration](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/bltdfa090c47077d5ac/684648589e3b4e6358b40634/GPU.jpg?width=1280&auto=webp&quality=80&disable=upscale)

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When it comes to the role of graphics processing units, or GPUs, in data centers, there are two things to know. The first is that GPUs are [critical to AI workloads](https://www.datacenterknowledge.com/data-center-chips/gtc-2025-nvidia-announces-next-generation-ai-superchips-) because they provide the massive compute resources necessary for AI training and inference, hence the rise of GPU-filled [data centers dedicated to AI](https://www.datacenterknowledge.com/ai-data-centers/ai-factories-separating-hype-from-reality). The second is that GPUs cost a lot of money. This is not just due to their price, which can be [tens of thousands of dollars](https://www.datacenterknowledge.com/ai-data-centers/amd-takes-on-nvidia-with-new-gpu-for-ai) per device, but also because of the energy they require to operate.

Put together, these factors mean that deploying AI workloads can be a pricey proposition. But in some cases, there’s a way to avoid breaking the bank. Instead of deploying GPUs in their own data centers, organizations can use cloud-based GPU hardware. That approach makes it possible to pay as you go – and to pay only for the GPU resources you consume. This is ideal for a business that needs access to GPUs on a temporary or intermittent basis.

Related:[Rack-Scale Revolution: AI Drives New Era of Data Center Architecture](https://www.datacenterknowledge.com/data-center-chips/rack-scale-revolution-ai-drives-new-era-of-data-center-architecture)

In contrast, for organizations that plan to use GPUs for an extended period, installing GPU-based servers in a private data center is likely to be more cost-effective. It may offer other benefits, too, like more control over GPU hardware configurations.

The challenge is deciding which [data center GPU](https://www.datacenterknowledge.com/build-design/graphics-processing-unit-gpu-) model is better: using cloud-based GPUs as a service or purchasing GPU hardware outright.

## The Case for Cloud GPUs for AI

Historically, the cloud was not typically a great solution for workloads that require specialized hardware, like GPUs, even if they were needed on a non-persistent basis. Although major public clouds, like Amazon Web Services and Microsoft Azure, have offered cloud server instances equipped with GPUs for over a decade, they were inexpensive and not very flexible. The standard advice for businesses that needed GPUs for non-AI-related tasks, like video rendering, was to deploy them in a private data center.

But AI workloads have arguably changed this calculus for two reasons. The first is that AI requires unprecedented levels of GPU-powered compute resources, so many that purchasing GPUs for deployment in a private data center is not as feasible. It’s reasonable enough to install GPU-enabled servers on-prem if you only need a few dozen of them. It starts to make less sense if you need thousands.

The other change is that cloud-based GPU offerings have now become more flexible. Instead of having only a handful of GPU instances to choose from, customers of cloud platforms like AWS can choose from [dozens](https://docs.aws.amazon.com/dlami/latest/devguide/gpu.html). The instances have also come down in price relative to other cloud server types. And beyond the big public clouds, a variety of smaller providers offer [GPU-as-a-Service](https://www.datacenterknowledge.com/cloud/gpu-as-a-service-what-it-pros-need-to-know) options.

Related:[Understanding GPU Servers and Their Role in Data Centers](https://www.datacenterknowledge.com/servers/understanding-gpu-servers-and-their-role-in-data-centers)

If you need massive amounts of GPUs, then turning to the cloud instead of purchasing your own GPUs can often make a lot of sense. It’s the easy, flexible way to train and run AI workloads.

## When to Build Your Own GPU Data Center

But just because cloud-based GPUs are a good fit for many organizations doesn’t make them ideal in all circumstances.

In general, organizations will be better served by purchasing their own GPUs for AI and deploying them on-prem or in a private data center if the following are true:

*   The AI workloads involve sensitive data, and the organization wants to keep the data off third-party infrastructure.  
*   It’s a priority to have access to the very latest GPUs, which aren’t always available from cloud providers.  
*   The organization needs full control over its GPU-enabled hardware. While bare metal GPU instances are available in the cloud, the configuration options available to customers are still limited, whereas with private servers, you can apply any configuration you want.  
*   Network latency issues could create performance problems if data has to move between cloud-based AI workloads and external data centers or client devices. Latency may prove to be an issue for AI-powered applications that require real-time performance, like streaming video analytics.  
*   Egress costs associated with moving data out of cloud-based AI servers would make the cloud less cost-effective.  

Related:[Heavy Compute: AI Data Centers Have a Weight Problem](https://www.datacenterknowledge.com/build-design/heavy-compute-ai-data-centers-have-a-weight-problem)

These factors could make private GPUs a better approach, even for AI workloads that don’t require continuous access to GPUs.

## The Hybrid GPU Option

It’s worth noting, too, that there’s no reason a business can’t use both approaches at the same time. It could run some AI workloads using cloud GPUs while running others on GPU-enabled servers in a private data center.

A hybrid GPU strategy often works well for organizations whose GPU requirements vary. For instance, it might make sense to use cloud GPUs for AI training, since training happens only periodically or for AI workloads that don’t process sensitive data. Meanwhile, those that run continuously or are subject to stricter compliance or security requirements could operate on private hardware.

Read more about:

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## About the Author

[![Image 19: Christopher Tozzi](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/bltb80c8a754e05f0af/6613d88533fe26f3ee4e5204/christophertozzi_2.jpg?width=400&auto=webp&quality=80&disable=upscale)](https://www.datacenterknowledge.com/author/christopher-tozzi)

[Christopher Tozzi](https://www.datacenterknowledge.com/author/christopher-tozzi)

Technology Analyst

Christopher Tozzi is a technology analyst with subject matter expertise in cloud computing, application development, open source software, virtualization, containers and more. He also lectures at a major university in the Albany, New York, area. His book, “For Fun and Profit: A History of the Free and Open Source Software Revolution,” was published by MIT Press.

[See more from Christopher Tozzi](https://www.datacenterknowledge.com/author/christopher-tozzi)

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