---
格式版本: 2
标题: "Are AI Neoclouds Rewiring Data Center Traffic Patterns?"
原文链接: "https://www.datacenterknowledge.com/infrastructure/are-ai-neoclouds-rewiring-data-center-traffic-patterns-"
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"
发布时间校准原因: "标题附近出现多个日期，其中Aug 11, 2026在strict_original_body中nearTitle，且多个来源一致，最可能是文章发布时间。"
发布时间校准置信度: "1"
发布时间候选数量: 46
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: "2026-08-12T03:58:53+08:00"
发布时间仲裁状态: "confirmed"
发布时间仲裁尝试次数: 1
发布时间仲裁耗时毫秒: 78723
发现时间: "2026-08-12T03:57:42+08:00"
入库时间: "2026-08-11T20:00:12.622Z"
来源平台: "Data Center Knowledge 搜索"
搜索渠道: "source_template"
搜索词: "https://www.datacenterknowledge.com/search?q=throughput"
匹配关键词:
  - "throughput"
  - "AI"
  - "GPU"
  - "Liquid Cooling"
  - "performance"
  - "bandwidth"
相关厂家:
  - "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: 10
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "正文为网站导航页，仅包含标题和列表，无有效文章内容，无法获取任何超节点/AI Rack相关技术或商业信息。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T04:00:18+08:00"
AI主题相关性: 2
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: 1
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/infrastructure/are-ai-neoclouds-rewiring-data-center-traffic-patterns-"
---

Title: Are AI Neoclouds Rewiring Data Center Traffic Patterns?

URL Source: https://www.datacenterknowledge.com/infrastructure/are-ai-neoclouds-rewiring-data-center-traffic-patterns-

Published Time: 2026-05-08T17:20:47.000Z

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# Are AI Neoclouds Rewiring Data Center Traffic Patterns?

Neocloud workloads are shifting data movement toward sustained, high-bandwidth transfers between storage and AI compute, according to a new industry report.

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[Shane Snider,](https://www.datacenterknowledge.com/author/shane-snider)Senior News Writer,Data Center Knowledge
May 8, 2026

3 Min Read

![Image 18: Neocloud illustration](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blt20a3497926d1d516/68cd7765c6305120ebc80e3a/Neocloud-Small.jpg?width=1280&auto=webp&quality=80&disable=upscale)

Image: Alamy

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New measurements suggest AI workloads are reshaping data center traffic patterns, consolidating network activity into fewer, larger, and more synchronized flows that sustain extremely high throughput.

Backblaze [reports](https://www.backblaze.com/blog/network-stats-for-q1-2026-neocloud-traffic-trends/) that neocloud traffic is driving sustained transfers between storage systems and compute clusters with observed rates ranging from 100 Gbps to 1 Tbps.

Sameh Boujelbene, vice president at Dell’Oro Group, said the shift is best understood by how these flows behave rather than how many exist. “It is better described as larger, more synchronized, lower entropy elephant traffic than simply fewer flows,” she said.

As a result, operators are rethinking how data center networks handle traffic. Instead of optimizing for large volumes of short-lived, distributed connections, AI clusters now depend on sustained, coordinated data movement among storage systems, GPUs, and backend fabrics. This places new pressure on switching, congestion management, and east-west network capacity in AI-focused facilities.

Related:[Networking Strains Under AI’s Scale Demands](https://www.datacenterknowledge.com/networking/the-breaking-points-networking-strains-under-ai-s-scale-demands)

A growing class of [AI-focused infrastructure providers](https://www.datacenterknowledge.com/ai-data-centers/neoclouds-vs-hyperscalers-will-ai-s-specialized-clouds-prevail-), including CoreWeave and Lambda Labs, is building GPU-dense environments optimized for high-throughput data movement between storage and compute.

## Concentrated, High Throughput Transfers

Within these environments, traffic is consolidating around fewer, more persistent endpoints tied to data movement between storage platforms and GPU infrastructure, according to Backblaze. Those connections carry larger volumes of data per flow, with a relatively small set of endpoints accounting for a rising share of total bytes transferred.

The activity also follows distinct phases: large datasets are first ingested into storage systems, then moved in bulk to compute clusters for training, followed by additional transfers to support inference and model updates.

## Training and Inference Drive Different Patterns

The drivers behind training and inference differ in ways that transport behavior. Javier Antich, principal AI engineer at Cisco Provider Connectivity Group, said that training preparation involves large transfers between enterprise data sources and the data centers where models run. Inside training clusters, multi-GPU workloads create “elephant flows” as GPUs exchange model state.

“Training jobs may last hours, days, or even weeks or months, depending on the size of the model,” Antich told Data Center Knowledge.

By contrast, inference traffic is more request-driven and bursty, even as aggregate demand rises, Boujelbene said.

Related:[Neocloud Storm Gathers as Data Center Deals Stall Over Credit Risk](https://www.datacenterknowledge.com/cloud/neocloud-storm-gathers-as-data-center-deals-stall-over-credit-risk)

Transport behavior is shifting as well. Antich said he is seeing increased use of QUIC, a transport protocol, for inference traffic. “QUIC aggregates multiple streams over a single connection, potentially aggregating more traffic than TCP,” Antich said. In Cisco’s measurements, QUIC accounts for 53% of total flows.

While individual inference requests are shorter, batching can still sustain high bandwidth utilization inside the data center fabric.

## Expanding Beyond the Data Center Fabric

Most training activity remains concentrated within the data center fabric, particularly in GPU-to-GPU communication, Antich said.

At the same time, data transfers tied to training preparation can extend across WAN links, and inference traffic increasingly travels beyond the data center as users and applications issue requests.

Agent-based AI workloads could further increase network demand by accelerating and multiplying interactions across systems, he added.

What began as a backend GPU fabric challenge is now spreading into broader Ethernet-based data center networks. As AI clusters adopt technologies such as RoCE and embrace more disaggregated architectures, the same traffic management issues are emerging across a broader portion of the infrastructure, Boujelbene said.

Related:[What AI Data Centers Can Learn from High-Frequency Trading](https://www.datacenterknowledge.com/networking/what-ai-data-centers-can-learn-from-high-frequency-trading)

Consequently, network design priorities are shifting from raw throughput toward managing synchronized traffic, avoiding congestion, and maintaining performance under load.

Backblaze said neocloud activity remains concentrated in the US East regions, including Northern Virginia, while expanding into additional regions such as Finland and Brazil.

“The data tells a clear story: AI is reshaping global infrastructure investment,” said Gleb Budman, CEO of Backblaze. “GPU clusters are concentrating not only in traditional high-intensity regions such as Northern Virginia and California, but also in Finland, Brazil, and beyond.”

Backblaze noted that the dataset is early and may not reflect long-term trends.

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## About the Author

[![Image 19: Shane Snider](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blt68d42dde3032e1e9/67212dce29bb88408c6ac9be/Shane_Snider_Profile.webp?width=400&auto=webp&quality=80&disable=upscale)](https://www.datacenterknowledge.com/author/shane-snider)

[Shane Snider](https://www.datacenterknowledge.com/author/shane-snider)

Senior News Writer, Data Center Knowledge

Shane Snider is Senior News Writer at Data Center Knowledge, covering AI infrastructure, hyperscale data centers, cloud platforms, and the power and energy systems driving modern compute expansion. His reporting focuses on the operational, economic, and environmental forces reshaping digital infrastructure, including AI factories, utility constraints, liquid cooling, renewable energy procurement, and next-generation data center architectures. He has won recent Azbee awards for news series and government reporting. Based in Raleigh, North Carolina, Snider covers how hyperscalers, utilities, chipmakers, and infrastructure providers are responding to the rapid rise of AI workloads and global compute demand.

You can reach Shane at [shane.snider@informatechtarget.com](mailto:shane.snider@informatechtarget.com) or on [LinkedIn](https://www.linkedin.com/in/shane-snider-1727755/).

[See more from Shane Snider](https://www.datacenterknowledge.com/author/shane-snider)

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