--- 格式版本: 2 标题: "Cloud vs. On-Prem AI Accelerators: Choosing the Best Fit for Your AI Workloads" 原文链接: "https://www.datacenterknowledge.com/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads" 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: "manual" 发布时间证据: "标题附近及正文多处出现Aug 11, 2026，且为最新日期，符合文章发布时间特征。" 发布时间校准原因: "标题附近及正文多处出现Aug 11, 2026，且为最新日期，符合文章发布时间特征。" 发布时间校准置信度: "manual-confirmed" 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: "2026-08-18T08:42:21+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: "2026-08-12T03:57:51+08:00" 入库时间: "2026-08-11T20:05:21.749Z" 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" - "performance" - "latency" 相关厂家: - "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: 15 AI分档: "非优质" AI质检状态: "不通过" AI打分理由: "页面仅为导航页，无实际正文内容，仅标题提及AI加速器，但无超节点/机柜级AI系统等有效信息，技术细节和商业信号缺失，完整性极低。" AI质检模型: "ali-deepseek-v4-flash" AI质检时间: "2026-08-12T04:05:28+08:00" AI主题相关性: 5 AI来源权威性: 5 AI新颖性: 2 AI技术细节: 0 AI商业部署信号: 0 AI完整性: 3 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/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads" --- Title: Cloud vs. On-Prem AI Accelerators: Choosing the Best Fit for Your AI Workloads URL Source: https://www.datacenterknowledge.com/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads Published Time: 2024-11-18T09:53:16.000Z Markdown Content: [![Image 1: Informa Logo](https://www.datacenterknowledge.com/assets/informaLogoCross-SPlaD0EU.png)Informa TechTarget|](https://www.informatechtarget.com/) [AI Business](https://aibusiness.com/ "AI Business")[SearchDataCenter](https://www.techtarget.com/searchdatacenter/ 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accelerators before deciding whether cloud or on-prem AI hardware aligns best with your workload requirements. [![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)[![Image 18: Picture of ITPro Today](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blt20256feca45e59f9/6647102a1bf9d635ca0389ed/ITP_ContributorPlaceholder.png?width=100&auto=webp&quality=80&disable=upscale)](https://www.datacenterknowledge.com/author/it-pro-today) [Christopher Tozzi,](https://www.datacenterknowledge.com/author/christopher-tozzi)[ITPro Today](https://www.datacenterknowledge.com/author/it-pro-today) November 18, 2024 4 Min Read ![Image 19: AI chip](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blt251b35edd1b27ec1/673e3f1cfc6a3a41d70aaf50/AI-chip-1716x965_-_2024-11-18.jpg?width=1280&auto=webp&quality=80&disable=upscale) Alamy [](http://www.facebook.com/sharer/sharer.php?u=https://www.datacenterknowledge.com/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads)[](https://www.linkedin.com/sharing/share-offsite/?url=https://www.datacenterknowledge.com/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads)[](http://www.twitter.com/intent/tweet?url=https://www.datacenterknowledge.com/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads)[](https://www.reddit.com/submit?url=https://www.datacenterknowledge.com/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads&title=Cloud%20vs.%20On-Prem%20AI%20Accelerators%3A%20Choosing%20the%20Best%20Fit%20for%20Your%20AI%20Workloads)[](https://bsky.app/intent/compose?text=Cloud%20vs.%20On-Prem%20AI%20Accelerators%3A%20Choosing%20the%20Best%20Fit%20for%20Your%20AI%20Workloads%20-%20https%3A%2F%2Fwww.datacenterknowledge.com%2Fcloud%2Fcloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads)[](mailto:?subject=Cloud%20vs.%20On-Prem%20AI%20Accelerators:%20Choosing%20the%20Best%20Fit%20for%20Your%20AI%20Workloads&body=I%20thought%20the%20following%20from%20DataCenterKnowledge%20might%20interest%20you.%0D%0A%0D%0A%20Cloud%20vs.%20On-Prem%20AI%20Accelerators%3A%20Choosing%20the%20Best%20Fit%20for%20Your%20AI%20Workloads%0D%0Ahttps%3A%2F%2Fwww.datacenterknowledge.com%2Fcloud%2Fcloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads) AI accelerators — meaning specialized hardware devices that are adept at supporting [artificial intelligence](https://www.itprotoday.com/ai-machine-learning/ai-basics-a-quick-reference-guide-for-it-professionals) workloads — tend to be expensive to purchase and operate on your own. That may make cloud-based AI accelerators seem like the perfect solution. Rather than having to buy your own AI hardware, why not just "rent" it from a cloud provider using an AI infrastructure-as-a-service (IaaS) model? In many cases, this approach is indeed preferable. But it also comes with some drawbacks. Read on for guidance on deciding whether cloud-based AI hardware is right for you. ## What Are AI Accelerators and AI Hardware? The terms "AI accelerator" and "AI hardware" refer to hardware devices that excel at AI tasks like model training and inference. In other words, they're devices other than generic computer processing units (CPUs), which can handle many types of AI workloads but which aren't particularly fast or efficient when working with AI. Related:[AI Accelerated Servers Fuel Growth in Data Center Spending](https://www.datacenterknowledge.com/servers/ai-accelerated-servers-fuel-growth-in-data-center-spending) [Graphical processing units](https://www.itprotoday.com/high-performance-computing/gpu-costs-aren-t-infinitely-scalable-what-can-be-done-) (GPUs) are one example of AI hardware. They're good for many types of AI workloads because they have a high core count, which allows them to process lots of data in parallel. But GPUs aren't the only type of AI accelerator. Other options include neutral processing units (NPUs), which are designed specifically for large-scale parallel computing for AI applications (whereas GPUs are designed mainly for rendering video but happen to be useful for certain AI tasks as well). Application-specific integrated chips (ASICs) and field programmable gate arrays (FGPAs) can also be good options for some AI workloads, such as processing data very quickly. ## On-Prem vs. Cloud-Based AI Hardware Like most types of hardware, AI accelerators can run either [on-prem or in the cloud](https://www.itprotoday.com/cloud-computing/4-reasons-to-choose-on-premise-vs-cloud-for-modern-workloads). An on-prem accelerator is one that you install in servers you manage yourself. This requires you to purchase the accelerator and a server capable of hosting it, set them up, and manage them on an ongoing basis. A cloud-based accelerator is one that a cloud vendor makes available to customers over the internet using an IaaS model. Typically, to access a cloud-based accelerator, you'd choose a cloud server instance designed for AI. For example, [Amazon](https://www.itprotoday.com/public-cloud/ai-drives-major-gains-for-big-3-cloud-giants-amazon-microsoft-google) offers EC2 cloud server instances that feature its Trainium AI accelerator chip. Google Cloud offers Tensor Processing Units (TPUs), another type of AI accelerator, as one of its cloud server options. ![Image 20: chart showing the pros and cons of cloud-based AI accelerators](https://eu-images.contentstack.com/v3/assets/blt07f68461ccd75245/blt1630c92f3d5abe79/673b15bd2202404b7ac00d90/Cloud-Based-AI-Accelerators.png?width=1400&auto=webp&quality=80&disable=upscale) ## The Benefits of Cloud AI Accelerators Why would you opt for a cloud-based accelerator instead of running one locally? The main benefits include: Related:[The Cloud Talent Crisis: Skills Shortage Drives Up Costs, Risks](https://www.datacenterknowledge.com/data-center-career-development/the-cloud-talent-crisis-skills-shortage-drives-up-costs-risks) No upfront cost: AI accelerators are typically pricey to purchase outright, with costs ranging from a few hundred dollars for a basic GPU to many tens of thousands of dollars for high-end GPUs and NPUs. Cloud-based accelerators allow companies to use AI hardware without having to pay for these devices upfront. Instead, they essentially rent them through an AI IaaS service. Pay for what you use: Along similar lines, cloud AI hardware lets users pay only for the hardware capacity they use. This is especially beneficial if you only need AI hardware for temporary tasks, like model training. Access to specialized AI hardware: Some types of AI accelerators are only available through the cloud. For instance, you can't purchase the AI chips developed by Amazon and Google for use in your own servers. You have to use cloud services to access them. Scalability: Like most cloud-based solutions, cloud AI hardware is very scalable. You can easily add more AI server instances if you need more processing power. This isn't the case with on-prem AI hardware, which is costly and complicated to scale up. ## The Drawbacks of AI Hardware in the Cloud On the other hand, cloud-based AI hardware can present some notable challenges: Performance limitations: Cloud-based AI workloads sometimes may not perform as well as those running on-prem due to the sharing of server hardware with other customers, as well as the potential for network latency when moving data into and out of the cloud. Related:[Creating the Next-Generation Cloud with Software-Defined App Services](https://www.datacenterknowledge.com/next-gen-data-centers/creating-the-next-generation-cloud-with-software-defined-app-services) Data privacy: You may need to work with highly sensitive data — if you're training a model using private information, for example. Since cloud-based AI requires you to store the data in a public cloud, this could increase the risk of accidentally exposing the data to third-party access. Cost: Although the upfront cost of cloud AI hardware is typically much lower than purchasing AI devices outright, your long-term costs could be higher, especially if you use the hardware extensively. In addition, cloud-based AI may require you to pay data egress fees, which don't apply on-prem. [Video 1](https://www.youtube.com/watch?v=BXLc6tnO920) ## When Are Cloud-Based AI Accelerators Worth It? So, should you use cloud-based AI accelerators? The answer boils down to which type of AI hardware you need and what you intend to use it for. If you'll be deploying AI workloads on an ongoing basis, purchasing your own hardware could make more sense. Likewise, on-prem AI is more feasible if you need less expensive devices. But for workloads that require highly specialized AI hardware, and/or workloads that will only operate on a temporary basis, the cloud is likely to be a better solution than on-prem AI. Read more about: [ITPro Today](https://www.datacenterknowledge.com/keyword/it-pro-today)[Technical Explainer](https://www.datacenterknowledge.com/keyword/technical-explainer) [](http://www.facebook.com/sharer/sharer.php?u=https://www.datacenterknowledge.com/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads)[](https://www.linkedin.com/sharing/share-offsite/?url=https://www.datacenterknowledge.com/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads)[](http://www.twitter.com/intent/tweet?url=https://www.datacenterknowledge.com/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads)[](https://www.reddit.com/submit?url=https://www.datacenterknowledge.com/cloud/cloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads&title=Cloud%20vs.%20On-Prem%20AI%20Accelerators%3A%20Choosing%20the%20Best%20Fit%20for%20Your%20AI%20Workloads)[](https://bsky.app/intent/compose?text=Cloud%20vs.%20On-Prem%20AI%20Accelerators%3A%20Choosing%20the%20Best%20Fit%20for%20Your%20AI%20Workloads%20-%20https%3A%2F%2Fwww.datacenterknowledge.com%2Fcloud%2Fcloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads)[](mailto:?subject=Cloud%20vs.%20On-Prem%20AI%20Accelerators:%20Choosing%20the%20Best%20Fit%20for%20Your%20AI%20Workloads&body=I%20thought%20the%20following%20from%20DataCenterKnowledge%20might%20interest%20you.%0D%0A%0D%0A%20Cloud%20vs.%20On-Prem%20AI%20Accelerators%3A%20Choosing%20the%20Best%20Fit%20for%20Your%20AI%20Workloads%0D%0Ahttps%3A%2F%2Fwww.datacenterknowledge.com%2Fcloud%2Fcloud-vs-on-prem-ai-accelerators-choosing-the-best-fit-for-your-ai-workloads) ## About the Authors [![Image 21: 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) [![Image 22: ITPro Today](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blt20256feca45e59f9/6647102a1bf9d635ca0389ed/ITP_ContributorPlaceholder.png?width=400&auto=webp&quality=80&disable=upscale)](https://www.datacenterknowledge.com/author/it-pro-today) [ITPro Today](https://www.datacenterknowledge.com/author/it-pro-today) [ITPro Today](http://www.itprotoday.com/), a sister site to Data Center Knowledge, is a leading online source of daily news, analysis and how-to’s about the information technology industry. We help IT professionals and technology stakeholders learn about, assess and manage the acquisition of next-gen technology that drives business innovation, including — but not limited to — analytics, artificial intelligence/machine learning, cloud computing, compute engines, containers, data center operations, edge computing, hyper-converged infrastructure, the mobile-first workplace, security, software development and DevOps, storage, and the Internet of Things. [See more from ITPro Today](https://www.datacenterknowledge.com/author/it-pro-today) [![Image 23: Add as a preferred source on Google](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blt2786692164e47749/69b46950fc6bd41335ba3b1b/google_preferred_source_badge_dark@2x.png?width=800&auto=webp&quality=80&disable=upscale)](https://google.com/preferences/source?q=https://www.datacenterknowledge.com/) ## Want more Data Center Knowledge stories in your Google search results? 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