Build the platform your AI workload actually needs. 3PS connects compute, GPU systems, storage, networking, software, security, procurement, and operations from specification through deployment.
500+ customers served.
Backed by 100+ professionals and technical resources across our delivery network.
Bring expected workloads, users, models, data location, and infrastructure constraints.
Specify compute, storage, networking, hosting, support, and acceptance for the intended workload.
We confirm the scope, deliverables, fees, and responsibilities before work begins.
DISCUSS THIS ENGAGEMENTA GPU specification is only one part of the design. Model size, inference traffic, concurrency, context length, data movement, availability, and the application shape the infrastructure requirement. The facility and operating team shape what can be deployed and sustained.
The engagement can cover a private AI environment, a dedicated inference platform, or infrastructure supporting a larger AI application. Evaluate on-premises, cloud, and hybrid options with the full workload and operating responsibility in view.
Establish the models, intended workload, users, expected demand, data access, and performance acceptance criteria. Use those requirements to assess compute, GPU memory, storage, and network capacity rather than sizing from a headline specification.
Evaluate GPU systems, including NVIDIA-based infrastructure where appropriate, together with host compute, power, cooling, rack space, networking, and hardware support. Confirm the installation dependencies before procurement.
Connect model hosting, deployment, identity, network segmentation, secrets, monitoring, and application access. Include compatibility, licensing, patching, and the operational implications of the selected software stack.
Coordinate the bill of materials, licensing, vendor requirements, staging, installation, and validation. Build documentation, capacity review, service ownership, and lifecycle planning into the delivery.
The workload assumptions, architecture, bill of materials, facility dependencies, and commercial scope.
Representative workload checks, access validation, integration results, and documented acceptance.
Monitoring, service responsibilities, maintenance, capacity planning, and recovery procedures.
Hardware selection and commercial availability are confirmed during scoping and procurement. Workload performance must be validated on the selected design. The bill of materials includes the agreed platform, licensing, and support requirements.
Yes. Procurement can be connected to architecture, staging, site installation, software integration, and ongoing operations within the engagement scope.
Start with the workload and its constraints. A short architecture and sizing exercise can establish whether existing infrastructure, cloud capacity, or a dedicated platform is the appropriate next step.
Bring the planned AI workload, preferred location, existing environment, and delivery timeline. We will define the infrastructure and the path to an accepted deployment.
PLAN YOUR AI INFRASTRUCTURE