On-premise local AI: hardware, models, and operation

It is to implement AI models and services in your own infrastructure (desktop box, mini workstation or GPU server) to reduce the cost of cloud tokens and keep sensitive data within the company's perimeter. It includes sizing, software, integration, and operation — not just buying hardware.

Artificial Intelligence is the hub of solutions (agents, chatbots, product RAG, automation). Private AI is the hub for where and how AI runs on-premises/on-prem. They complement each other: many AI hub solutions can be deployed on top of the infrastructure we implement here.

"Private and secure AI" covers isolation architectures (VPC/VNet, dedicated cloud, residence). Private AI on-premise focuses on on-premises DGX Spark/GB10 or AMD Halo enclosures/servers, CAPEX, and LM Studio→vLLM stack. They can be combined in a hybrid design.

We include the GB10 family of OEMs (ASUS Ascent GX10, Dell, HP, Lenovo, MSI, Gigabyte, Acer) and the AMD family (Ryzen AI Halo/Strix Halo and equivalent mini-PCs). We recommend according to load, CUDA vs ROCm, Windows/Linux and customer purchase support.

By default we design and integrate; Provisioning can be from the customer, a partner, or channel. We do not represent ourselves as an official NVIDIA/AMD reseller unless explicitly agreed. The value is in the implementation project.

LM Studio is ideal for desktop adoption: downloading models, local chat, and serving an OpenAI-compatible API without setting up a cluster. In multi-user production we usually switch to vLLM or TensorRT-LLM. The pilot→production path is part of the service.

When the volume of tokens is low, the data is not sensitive and there is no capacity to operate infrastructure. In those cases, a managed cloud service can be simpler and cheaper. Initial diagnosis avoids unnecessary CAPEX.

Yes. A common pattern is to prototype on DGX Spark/GB10 or Halo, validate models and processes, and scale to multi-GPU or hybrid cloud server when concurrency demands it. We designed the software not to tie you to a single form factor.

Cost and risk diagnosis, hardware proposal, runtime deployment, model loading, internal API, pilot case (RAG or agent), basic governance, operation and transfer documentation. The scope is limited to the offer.

We define access, retention, auditing, and data perimeter. We coordinate with the client's privacy/legal. We do not promise automatic certification: we facilitate controls and operational evidence.