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build 6ddc7a35 | content blog-content@417154f · 338 posts | profiles 32 · corpus 315 | ● 0 skipped | built 2026-10-21 12:35 UTC format run

$ soultec get solution/hpe/hpe-private-cloud-ai -o yaml$ soultec get solution/hpe/hpe-private-cloud-ai -o json$ soultec get solution/hpe/hpe-private-cloud-ai -o toml$ soultec get solution/hpe/hpe-private-cloud-ai -o xml$ python3 hpe/hpe-private-cloud-ai.pyPS> ./hpe/hpe-private-cloud-ai.ps1$ go run hpe/hpe-private-cloud-ai.go$ node hpe/hpe-private-cloud-ai.js

apiVersion: soultec.ch/v1kind: Solutionmetadata: name: hpe/hpe-private-cloud-ai locale: en labels: vendor: HPE Private Cloud AI capability/ai: 5.07 capability/cloud: 1.92 vendor/hpe: 1.99 annotations: source: src/content/solutions/en/hpe/hpe-private-cloud-ai.md route: /en/solutions/hpe/hpe-private-cloud-ai/ schema: /nerd/schema/solutions.json markdown: /en/solutions/hpe/hpe-private-cloud-ai.mdspec: title: HPE Private Cloud AI tags: [ai, cloud] vendors: [hpe] summary: >- A preconfigured AI platform from HPE and NVIDIA, in your own datacentre. Data scientists take tools and models through self-service, and the data stays in the building. photoNeed: >- HPE hardware in our hands: a ProLiant or Alletra being racked, cabled, or opened, with the model badge readable stub: false draft: false kind: product addon: false vendorName: HPE Private Cloud AI status: current sections: - heading:

What it is

body: | Private Cloud AI is an AI-optimised private cloud that HPE built together with NVIDIA. Preconfigured hardware: ProLiant servers, NVIDIA GPUs, networking and storage matched to AI workloads and scalable across several sizes. On top sits a self-service layer where data scientists take tools and models without raising a request for every experiment. - heading:

What it is for

body: | Organisations that want to do productive work with AI and whose data cannot leave the building. - heading:

Preconfiguration

body: | Building an AI platform yourself means reconciling GPU drivers, framework versions, network throughput and storage latency before a single model runs. In Private Cloud AI that reconciliation has already been done. - heading:

What to watch

body: | GPUs are expensive and want to be busy. Before size and quantity are fixed, it should be clear how many teams share the platform and how allocation is governed.status: corpus: 315 services: - {ref: services/cloud, score: 0.39} - {ref: services/it-architecture, score: 0.29} experts: - {ref: experts/karl-widmer, score: 0.40} - {ref: experts/daniel-stadelmann, score: 0.39} - {ref: experts/oliver-erismann, score: 0.38} neighbours: - {ref: solutions/hpe, relation: parent} - {ref: solutions/hpe/hpe-private-cloud-business-edition-pcbe, score: 0.46} - {ref: solutions/hpe/hpe-morpheus/vm-essentials, score: 0.42} - {ref: solutions/hpe/hpe-greenlake, score: 0.41} search: /en/search.json

{ "apiVersion": "soultec.ch/v1", "kind": "Solution", "metadata": { "name": "hpe/hpe-private-cloud-ai", "locale": "en", "labels": { "vendor": "HPE Private Cloud AI", "capability/ai": 5.07, "capability/cloud": 1.92, "vendor/hpe": 1.99 }, "annotations": { "source": "src/content/solutions/en/hpe/hpe-private-cloud-ai.md", "route": "/en/solutions/hpe/hpe-private-cloud-ai/", "schema": "/nerd/schema/solutions.json", "markdown": "/en/solutions/hpe/hpe-private-cloud-ai.md" } }, "spec": { "title": "HPE Private Cloud AI", "tags": [ "ai", "cloud" ], "vendors": [ "hpe" ], "summary": "A preconfigured AI platform from HPE and NVIDIA, in your own datacentre. Data scientists take tools and models through self-service, and the data stays in the building.", "photoNeed": "HPE hardware in our hands: a ProLiant or Alletra being racked, cabled, or opened, with the model badge readable", "stub": false, "draft": false, "kind": "product", "addon": false, "vendorName": "HPE Private Cloud AI", "status": "current", "sections": [ { "heading": "

What it is

", "body": "Private Cloud AI is an AI-optimised private cloud that HPE built together with NVIDIA.\nPreconfigured hardware: ProLiant servers, NVIDIA GPUs, networking and storage matched to AI\nworkloads and scalable across several sizes.\n\nOn top sits a self-service layer where data scientists take tools and models without\nraising a request for every experiment." }, { "heading": "

What it is for

", "body": "Organisations that want to do productive work with AI and whose data cannot leave the\nbuilding." }, { "heading": "

Preconfiguration

", "body": "Building an AI platform yourself means reconciling GPU drivers, framework versions, network\nthroughput and storage latency before a single model runs. In Private Cloud AI that\nreconciliation has already been done." }, { "heading": "

What to watch

", "body": "GPUs are expensive and want to be busy. Before size and quantity are fixed, it should be\nclear how many teams share the platform and how allocation is governed." } ] }, "status": { "corpus": 315, "services": [ { "ref": "services/cloud", "score": "0.39" }, { "ref": "services/it-architecture", "score": "0.29" } ], "experts": [ { "ref": "experts/karl-widmer", "score": "0.40" }, { "ref": "experts/daniel-stadelmann", "score": "0.39" }, { "ref": "experts/oliver-erismann", "score": "0.38" } ], "neighbours": [ { "ref": "solutions/hpe", "relation": "parent" }, { "ref": "solutions/hpe/hpe-private-cloud-business-edition-pcbe", "score": "0.46" }, { "ref": "solutions/hpe/hpe-morpheus/vm-essentials", "score": "0.42" }, { "ref": "solutions/hpe/hpe-greenlake", "score": "0.41" } ], "search": "/en/search.json" }}

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What it is

"body = '''Private Cloud AI is an AI-optimised private cloud that HPE built together with NVIDIA.Preconfigured hardware: ProLiant servers, NVIDIA GPUs, networking and storage matched to AIworkloads and scalable across several sizes.On top sits a self-service layer where data scientists take tools and models withoutraising a request for every experiment.'''[[spec.sections]]heading = "

What it is for

"body = '''Organisations that want to do productive work with AI and whose data cannot leave thebuilding.'''[[spec.sections]]heading = "

Preconfiguration

"body = '''Building an AI platform yourself means reconciling GPU drivers, framework versions, networkthroughput and storage latency before a single model runs. In Private Cloud AI thatreconciliation has already been done.'''[[spec.sections]]heading = "

What to watch

"body = '''GPUs are expensive and want to be busy. Before size and quantity are fixed, it should beclear how many teams share the platform and how allocation is governed.'''[status]corpus = 315search = "/en/search.json"[[status.services]]ref = "services/cloud"score = "0.39"[[status.services]]ref = "services/it-architecture"score = "0.29"[[status.experts]]ref = "experts/karl-widmer"score = "0.40"[[status.experts]]ref = "experts/daniel-stadelmann"score = "0.39"[[status.experts]]ref = "experts/oliver-erismann"score = "0.38"[[status.neighbours]]ref = "solutions/hpe"relation = "parent"[[status.neighbours]]ref = "solutions/hpe/hpe-private-cloud-business-edition-pcbe"score = "0.46"[[status.neighbours]]ref = "solutions/hpe/hpe-morpheus/vm-essentials"score = "0.42"[[status.neighbours]]ref = "solutions/hpe/hpe-greenlake"score = "0.41"

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What it is

</heading> <body>Private Cloud AI is an AI-optimised private cloud that HPE built together with NVIDIA.Preconfigured hardware: ProLiant servers, NVIDIA GPUs, networking and storage matched to AIworkloads and scalable across several sizes.On top sits a self-service layer where data scientists take tools and models withoutraising a request for every experiment. </body> </section> <section> <heading>

What it is for

</heading> <body>Organisations that want to do productive work with AI and whose data cannot leave thebuilding. </body> </section> <section> <heading>

Preconfiguration

</heading> <body>Building an AI platform yourself means reconciling GPU drivers, framework versions, networkthroughput and storage latency before a single model runs. In Private Cloud AI thatreconciliation has already been done. </body> </section> <section> <heading>

What to watch

</heading> <body>GPUs are expensive and want to be busy. Before size and quantity are fixed, it should beclear how many teams share the platform and how allocation is governed. </body> </section> </sections> </spec> <status> <corpus>315</corpus> <services> <item> <ref>services/cloud</ref> <score>0.39</score> </item> <item> <ref>services/it-architecture</ref> <score>0.29</score> </item> </services> <experts> <item> <ref>experts/karl-widmer</ref> <score>0.40</score> </item> <item> <ref>experts/daniel-stadelmann</ref> <score>0.39</score> </item> <item> <ref>experts/oliver-erismann</ref> <score>0.38</score> </item> </experts> <neighbours> <item> <ref>solutions/hpe</ref> <relation>parent</relation> </item> <item> <ref>solutions/hpe/hpe-private-cloud-business-edition-pcbe</ref> <score>0.46</score> </item> <item> <ref>solutions/hpe/hpe-morpheus/vm-essentials</ref> <score>0.42</score> </item> <item> <ref>solutions/hpe/hpe-greenlake</ref> <score>0.41</score> </item> </neighbours> <search>/en/search.json</search> </status></manifest>

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What it is

", "body": ( "Private Cloud AI is an AI-optimised private cloud that HPE built together with NVIDIA.\n" "Preconfigured hardware: ProLiant servers, NVIDIA GPUs, networking and storage matched to AI\n" "workloads and scalable across several sizes.\n" "\n" "On top sits a self-service layer where data scientists take tools and models without\n" "raising a request for every experiment." ), }, { "heading": "

What it is for

", "body": ( "Organisations that want to do productive work with AI and whose data cannot leave the\n" "building." ), }, { "heading": "

Preconfiguration

", "body": ( "Building an AI platform yourself means reconciling GPU drivers, framework versions, network\n" "throughput and storage latency before a single model runs. In Private Cloud AI that\n" "reconciliation has already been done." ), }, { "heading": "

What to watch

", "body": ( "GPUs are expensive and want to be busy. Before size and quantity are fixed, it should be\n" "clear how many teams share the platform and how allocation is governed." ), }, ], }, "status": { "corpus": 315, "services": [ { "ref": "services/cloud", "score": "0.39", }, { "ref": "services/it-architecture", "score": "0.29", }, ], "experts": [ { "ref": "experts/karl-widmer", "score": "0.40", }, { "ref": "experts/daniel-stadelmann", "score": "0.39", }, { "ref": "experts/oliver-erismann", "score": "0.38", }, ], "neighbours": [ { "ref": "solutions/hpe", "relation": "parent", }, { "ref": "solutions/hpe/hpe-private-cloud-business-edition-pcbe", "score": "0.46", }, { "ref": "solutions/hpe/hpe-morpheus/vm-essentials", "score": "0.42", }, { "ref": "solutions/hpe/hpe-greenlake", "score": "0.41", }, ], "search": "/en/search.json", },}if __name__ == "__main__": print(json.dumps(PAGE, indent=2, ensure_ascii=False))

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What it is

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What it is for

' body = @'Organisations that want to do productive work with AI and whose data cannot leave thebuilding.'@ } [ordered]@{ heading = '

Preconfiguration

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What to watch

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What it is

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What it is for

", "body": "Organisations that want to do productive work with AI and whose data cannot leave the\n" + "building.", }, map[string]any{ "heading": "

Preconfiguration

", "body": "Building an AI platform yourself means reconciling GPU drivers, framework versions, network\n" + "throughput and storage latency before a single model runs. In Private Cloud AI that\n" + "reconciliation has already been done.", }, map[string]any{ "heading": "

What to watch

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What it is

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What it is for

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Preconfiguration

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What to watch

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Solution · HPE

HPE Private Cloud AI

A preconfigured AI platform from HPE and NVIDIA, in your own datacentre. Data scientists take tools and models through self-service, and the data stays in the building.

HPE Gold Partner

Topics AI 5.07 Cloud 1.92

Vendors HPE 1.99

02Services

02Capabilities

315Corpus

photograph wantedHPE hardware in our hands: a ProLiant or Alletra being racked, cabled, or opened, with the model badge readable

What it is

Private Cloud AI is an AI-optimised private cloud that HPE built together with NVIDIA. Preconfigured hardware: ProLiant servers, NVIDIA GPUs, networking and storage matched to AI workloads and scalable across several sizes.

On top sits a self-service layer where data scientists take tools and models without raising a request for every experiment.

What it is for

Organisations that want to do productive work with AI and whose data cannot leave the building.

Preconfiguration

Building an AI platform yourself means reconciling GPU drivers, framework versions, network throughput and storage latency before a single model runs. In Private Cloud AI that reconciliation has already been done.

What to watch

GPUs are expensive and want to be busy. Before size and quantity are fixed, it should be clear how many teams share the platform and how allocation is governed.