Dell AI Factory vs. HPE Private Cloud AI
Start with HPE Private Cloud AI if your private inference workload fits an HPE bundle. Start with Dell AI Factory if you need to design the GPU cluster around your training workload or your data center. Give both resellers the same test before you buy.
Put the same job in front of both teams. If you're building an AI assistant that answers questions from company documents, ask each reseller to run the same model against the same document set. If you are training across multiple GPU servers, ask each team to show how the network and storage keep those GPUs fed.
Which private AI proposal should you ask for first?
| Your job | Dell AI Factory with NVIDIA | HPE Private Cloud AI |
|---|---|---|
Private inference and answers from company documents |
Start with Dell's validated inference design. Have the reseller name the servers, software and integration work in its quote. |
Start with the HPE bundle sized for your use case. HPE's configuration guide identifies which packages are intended for inference and document retrieval. |
Fine-tuning and multi-node training |
Dell publishes designs for training and fine-tuning on a cluster with dedicated GPU networking. Ask for the design sized to your job. |
HPE lists fine-tuning for its Large configuration. Ask HPE to confirm a supported design for your actual training run. |
Deployment and day-two support |
Name who owns the cluster software, network, storage and NVIDIA support under this proposal. |
Name who owns the HPE AI Essentials stack, software updates and application support under this proposal. |
Compare Dell and HPE on Private Inference
Inference is the work of running an AI model to answer a request. When the answer needs facts from your own files, test the model with retrieval-augmented generation, or RAG. That means it looks up relevant documents before answering. HPE's configuration guide distinguishes a sandbox, inference configurations and larger configurations for RAG and fine-tuning. Dell's validated inference design gives you a starting architecture the reseller can size to the same workload.
Use real request patterns with a permitted set of documents. Ask both teams to report response time under load and show whether the answer points to the right document. Include a permissions test so you can see what happens when two users have access to different files.
Size Dell and HPE for Fine-Tuning or Training
Fine-tuning adapts an existing model. Training a large model across many servers can place a different load on storage and the GPU network. Dell publishes a reference design for training, fine-tuning and inference that combines PowerEdge, NVIDIA GPUs and networking, and PowerScale storage. HPE identifies fine-tuning as a use case for its Large configuration. Get the exact supported HPE design in writing if your project calls for a larger training cluster.
Give the reseller your model, the data size and the time window for a run. Ask for the proposed GPU count, network design and how long it takes to save and resume a run. Have both teams show what you would need to buy if the workload outgrows the first cluster.
Put Dell and HPE Deployment and Support in Writing
HPE documents disconnected configurations for Private Cloud AI. If the site must run without an internet connection, ask each reseller to show how licenses, model files, software updates and support work in that environment. Get a list of every connection the proposed design needs.
- Use one workload brief. Specify the model, your document or training data, expected load and where the system has to run.
- Ask for the full bill of materials and site requirements. Include software subscriptions, networking, rack power and cooling.
- Ask for an acceptance test you can rerun. Use the same test data and pass conditions in both proposals.
- Name the team responsible for installation, upgrades and the first incident after go-live. Put the handoff and escalation contacts in the statement of work.
Take the same workload brief to the AI infrastructure reseller shortlist. Ask each team which parts of the proposed Dell or HPE configuration it can show in a lab and which parts it will support after installation. If your design is still open to a Cisco-led network, start with the three-vendor AI infrastructure comparison.