ON-PREMISE LOCAL AI: HARDWARE, MODELS, AND OPERATION
When is it worth switching AI to on-premises?
We measure token cost, data risk, and operational capacity before recommending a box or server.
What is Cloud vs on-prem diagnostics and hardware sizing?
Moving to on-premise AI without diagnostics is the fastest way to spend CAPEX with no return. Q2BSTUDIO starts with actual usage: which processes consume tokens, how often, what latency they need, what data they touch, and who should operate the system. We built a projected cloud cost scenario versus local TCO (hardware, electricity, maintenance, staffing) and a data risk map.
Sizing is not "the most expensive box". For prototypes and small teams, a GB10 mini workstation (DGX Spark or OEM such as ASUS GX10) or AMD Ryzen AI Halo / Strix Halo is usually enough. For high concurrency or heavier models we evaluate NVIDIA or AMD Instinct GPU servers. We document assumptions, limits, and scaling criteria.
We deliver executive report, architecture recommendation, shortlist of multi-vendor hardware and pilot→production phase plan. We don't sell a brand; We help decide with real numbers and constraints.
FEATURES
Features of Cloud vs on-prem diagnostics and hardware sizing
Inventory of AI Uses
Processes, estimated volume, and data sensitivity.
Cloud cost model
Token projection and growth scenarios.
TCO on-prem
Hardware, power, maintenance, and operation.
Shortlist hardware
GB10 OEM, AMD Halo and GPU servers compared.
Scaling criteria
When to move from box to cluster or hybrid.
ENS/GDPR requirements
Perimeter, accesses and retention in the design.
Operational risks
Patches, backups, continuity and team skills.
Roadmap
Phases, dependencies and quick wins.
FREQUENTLY ASKED QUESTIONS
Frequently asked questions about Cloud vs on-prem diagnostics and hardware sizing
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