On-Prem AI Hardware
Based on your model, user count, and data volume, we pick servers, GPUs, and storage that are just enough.
Book a free consultation→Based on your model, user count, and data volume, we pick servers, GPUs, and storage that are just enough.
Who it is for
- Companies that have decided to keep AI in-house and need to buy hardware
- Teams that already bought equipment but aren't getting the performance they expected
- Organizations that want an internal knowledge base, document search, or image recognition
- Management that needs to weigh the long-term cost of building in-house versus using the cloud
What we do
Workload analysis
Confirm which models will run, how many people use them at once, and how fast responses must be.
Compute and memory
Estimate GPU memory and compute from model size so you do not buy the wrong tier.
Storage and backup
Plan NAS or server storage capacity, backup strategy, and data tiering.
Network and security
Secure design for internal network isolation, access rights, and remote connections.
Power and cooling
Estimate power draw and cooling needs, and check whether your server room or office can handle them.
Cost comparison
Compare total cost of ownership of in-house versus cloud over three to five years.
How it works
Requirements interview
Use cases and expected scale.
Load estimate
Models, users, and data volume.
Option comparison
Combinations of specs and budgets.
Hands-on testing
Test performance with real models when needed.
Purchase recommendation
Deliver the spec sheet and build recommendations.
What you get
Hardware spec recommendation
Server, GPU, storage, and network specs.
Budget and total cost of ownership
Estimate of purchase, electricity, and maintenance costs.
Deployment architecture diagram
Equipment layout, network, and backup architecture.
FAQ
Do we have to buy a GPU?
It depends on the use. Document search and small models can run on an ordinary server or a high-memory PC; you only need a GPU for real-time generation or many simultaneous users.
Can a NAS run AI?
A NAS works well as the storage hub for data and models, paired with a separate compute host. We plan around your existing equipment to reuse what you already own.
Is AI hardware power-hungry?
A GPU at full load draws a lot. We estimate electricity and cooling during selection and, where needed, add energy-saving schedules; this is where our energy background helps.
Related services
AI Adoption Planning
Map your processes, find the work truly worth applying AI to, and set the order before building anything.
On-Premise AI Deployment
Build AI applications on your own equipment, so your data never leaves the company.
AI × Energy Management
Use AI to catch abnormal usage and forecast peak load, making energy savings and storage dispatch more precise.
From one kWh to one tonne.
Tell us about your situation. We reply with initial advice within 3 business days.