---
格式版本: 2
标题: "Nutanix Targets Neocloud AI Economics With Platform Push"
原文链接: "https://www.datacenterknowledge.com/cloud/nutanix-targets-neocloud-ai-economics-with-platform-push"
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: "标题附近明确标注的发布时间为Aug 11, 2026，且本地原始正文与爬取markdown正文均一致，优先级最高。"
发布时间校准置信度: "1"
发布时间候选数量: 38
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: "2026-08-12T03:59:31+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: 50778
发现时间: "2026-08-12T03:57:42+08:00"
入库时间: "2026-08-11T20:00:21.822Z"
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"
  - "deployment"
  - "performance"
  - "latency"
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: 0
AI分档: "非优质"
AI质检状态: "不通过"
AI打分理由: "文章为Nutanix平台推新的一般性报道，正文缺失，仅标题和摘要提及AI经济，未涉及超节点/AI Rack/机柜级AI系统、架构、互连、供电、散热等核心技术或商业落地，与项目关注范围无关。"
AI质检模型: "ali-deepseek-v4-flash"
AI质检时间: "2026-08-12T04:00:26+08:00"
AI主题相关性: 0
AI来源权威性: 0
AI新颖性: 0
AI技术细节: 0
AI商业部署信号: 0
AI完整性: 0
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/nutanix-targets-neocloud-ai-economics-with-platform-push"
---

Title: Nutanix Targets Neocloud AI Economics With Platform Push

URL Source: https://www.datacenterknowledge.com/cloud/nutanix-targets-neocloud-ai-economics-with-platform-push

Published Time: 2026-04-07T18:30:00.000Z

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# Nutanix Targets Neocloud AI Economics With Platform Push

Nutanix is positioning itself at the center of a new class of infrastructure providers – and a new set of economic challenges driven by AI.

[![Image 17: Picture of Shane Snider](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blt68d42dde3032e1e9/67212dce29bb88408c6ac9be/Shane_Snider_Profile.webp?width=100&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
April 7, 2026

5 Min Read

![Image 18](https://eu-images.contentstack.com/v3/assets/blt8eb3cdfc1fce5194/blt0f2e8f9bb783b9ab/69d5201a88b7182c69a70e6b/2S8PNRF.jpg?width=1280&auto=webp&quality=80&disable=upscale)

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Speaking ahead of the company’s Next conference in Chicago this week, Nutanix outlined a broad expansion of the Nutanix Cloud Platform (NCP), which the company described as “the authentic AI era.”

The latest updates span AI infrastructure, Kubernetes, service provider tooling, and ecosystem partnerships – but they center on a single goal: helping enterprises and emerging neocloud providers manage increasingly unpredictable AI workloads.

Nutanix is explicitly targeting what it calls the “agentic AI era,” where autonomous systems generate continuous, unpredictable demand.

The updates include Kubernetes on bare metal through NKP Metal, expanded service provider tooling via SP Central for multi-tenant AI infrastructure, and deeper integration across storage, data services, and cloud environments. Nutanix is also extending its ecosystem with partners including NetApp, Cisco, and MongoDB, while adding capabilities such as zero-copy migration from legacy virtualization platforms and expanded sovereign cloud support. Together, the changes are designed to position Nutanix as a control layer for enterprise and provider AI environments.

Related:[Cloud Region Migration: Optimize Costs, Latency, and Compliance](https://www.datacenterknowledge.com/cloud/cloud-region-migration-optimize-costs-latency-and-compliance)

At the same time, Nutanix is stepping into a layer where hyperscalers control supply and specialized providers are optimizing aggressively for performance and cost – raising the stakes for whether platform-level control can differentiate.

## AI Workloads Expose a New Problem

Lee Caswell, senior vice president of products and solutions marketing, pointed to a growing issue in AI environments: unpredictable resource consumption.

“The biggest pain point we’re hearing is about unpredictable token generation and consumption,” he said during a recent press briefing. “Tokens are now the new metric for GPU consumption.”

AI inference and agent-driven systems generate highly variable demand, with token usage – and GPU utilization – spiking in unpredictable patterns. That complicates capacity planning as well as cost control.

But the market itself is evolving, and that transition is reshaping how value is captured across the stack.

“The AI infrastructure market has shifted from a period of ‘GPU scarcity’ to one of ‘operational maturity,’” said Steven Dickens, CEO and analyst at HyperFrame Research.

## A “Winner Takes Most” Neocloud Market

A central element of Nutanix’s strategy is its push into the GPU cloud provider market. These [neocloud operators](https://www.datacenterknowledge.com/cloud/neocloud-storm-gathers-as-data-center-deals-stall-over-credit-risk) deliver shared AI infrastructure as monetizable services, and Dickens said that this nascent market is already consolidating.

Related:[Omdia: The Agentic Era Collapses Cloud Layers](https://www.datacenterknowledge.com/cloud/omdia-the-agentic-era-collapses-cloud-layers)

“We are seeing a ‘winner takes most’ dynamic where a small circle – likely five to seven providers – will control the majority of non-hyperscale AI compute,” he said. “Most neocloud providers won’t survive the transition.”

He pointed to capital intensity, access to next-generation silicon and specialized software stacks as the drivers. Companies like CoreWeave have established an early lead by treating GPUs as financial assets rather than infrastructure.

That creates a narrow window for platform providers like Nutanix: enable the winners – or be sidelined as the market concentrates.

## Token Economics: From Infrastructure to Yield

The shift toward token-based consumption is central to both Nutanix’s positioning and the broader market.

“Token-based consumption is a fundamental shift in unit economics,” Dickens said. “For the first time, infrastructure has a ‘yield.’ In traditional cloud, you paid for the ‘room’ – the VM or container. In the token economy, you pay for the ‘meal’ – the output.”

Operators are increasingly focused on throughput – how many tokens can be generated per unit of time and cost – rather than uptime alone. According to Dickens, this is forcing a much tighter integration between hardware and the inference engines.

Related:[Earnings Roundup: Neoclouds Shift From GPU Race to Power Wars](https://www.datacenterknowledge.com/cloud/earnings-roundup-neoclouds-shift-from-gpu-race-to-power-wars)

Inefficiencies in storage, networking, or orchestration now directly reduce GPU output. As such, Nutanix is positioning storage as a performance layer for “[AI factories](https://www.datacenterknowledge.com/ai-data-centers/ai-factories-separating-hype-from-reality),” where throughput determines how efficiently models are trained and served.

Enhancements to its unified storage platform, including expanded object capabilities and planned RDMA acceleration, target data-intensive AI pipelines. However, whether those optimizations deliver meaningful gains – as hyperscalers and specialized providers pursue similar strategies – remains an open question.

## Orchestration and Sovereignty

As infrastructure becomes more abstracted, control is shifting up the stack.

“The real control point is emerging at the orchestration and governance layer,” Dickens said.

While hyperscalers dominate physical infrastructure, enterprise loyalty is increasingly tied to the management of data and AI agents. For Dickens, the “lock-in” happens at the point where enterprise data meets the model. This is driving demand for “sovereign AI” environments – platforms that allow enterprises to move between models without disrupting data pipelines or violating regulations.

Within that landscape, Dickens sees a defined opportunity for Nutanix. Rather than competing on hyperscale, the company is targeting enterprises that need tighter control over data, cost and deployment. New capabilities like SP Central are designed to turn shared GPU clusters into governed, monetizable service layers.

“By collapsing the complexity of GPU partitioning, liquid cooling, and model deployment into their framework, they are offering a cloud-on-prem experience,” Dickens said.

Nutanix is also lowering the barrier for customers transitioning away from traditional virtualization platforms, with zero-copy migration capabilities that allow near-instant conversion without duplicating data.

## From Cloud-First to “AI Smart”

If Nutanix can integrate on-premises environments and neocloud capacity, it could position itself at a key control point for enterprise AI adoption. But that depends on execution – and whether enterprises prioritize control over access to hyperscale infrastructure.

The company is positioning flexibility as a hedge against hardware constraints, emphasizing broader infrastructure support and the reuse of existing investments. And as AI moves into production, the challenge is not just accessing compute, but also controlling how it is consumed, measured, and monetized.

“We’re seeing a similar transition here of going AI first to AI smart,” Caswell said.

That shift is likely to reduce margins for general-purpose cloud models and accelerate consolidation among providers that cannot optimize for token-level efficiency.

Nutanix is betting that enterprises will prioritize control. For the broader market, the outcome is likely to be more concentrated – and less forgiving.

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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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