Workloads & models
Define what the system must actually do and which model families are appropriate.
Infrastructure designed around the workload
AI-Ready Infrastructure for Governed Intelligence
Connect models, memory, accelerated compute, storage, networking, security and governance around the work your organization actually needs to perform.
Architecture before hardware
Design the environment around the work
AI infrastructure should begin with workloads, models, memory requirements, information sensitivity, latency, user experience and operating responsibility—not with a predetermined hardware list.
AFA helps organizations connect workload design to model choice, GPU memory and compute, storage, networking, identity, security, governance and operations.
More compute is not automatically better capability.
Infrastructure stack
A connected architecture for intelligence
The right architecture may combine enterprise cloud services, private environments, local inference and larger accelerated-computing systems.
Memory & compute
Size GPU memory, accelerators and system capacity around inference, adaptation, multimodal work and concurrency.
Storage & networking
Design data movement, model storage, retrieval and high-speed connectivity around real operating needs.
Security & governance
Connect identity, permissions, logs, retention, data classification and model policy to the infrastructure.
Sovereign placement
Route sensitive or jurisdictionally constrained workloads to environments with the required control.
Physical AI readiness
Plan for simulation, robotics, perception, edge inference and coordination when intelligence enters physical systems.
Reference capability
A practical environment for testing before scale
The proposed AFA Sovereign AI & Physical AI Reference Lab is intended to let AFA and customers test architecture assumptions using Dell Technologies and NVIDIA accelerated-computing infrastructure.
The objective is to develop practical evidence around model behaviour, private inference, knowledge grounding, adaptation, agents, accelerated workloads, simulation and Physical AI rather than recommending infrastructure from specifications alone.
Architecture lifecycle
Workload → model → infrastructure → operations
Infrastructure decisions should remain traceable to a real organizational requirement.
Assess workloads
Inventory users, use cases, models, data classes and performance needs.
Model capacity
Estimate memory, throughput, latency, concurrency and adaptation requirements.
Design topology
Map compute, storage, networking, cloud/private boundaries and identity controls.
Validate
Benchmark representative workloads and test governance before procurement or scale.
Operate
Establish monitoring, lifecycle, change management, support and capacity planning.
Continue the journey
Related pathways
Practical perspective
From understanding to practical action
Atkinson connects people, policy, infrastructure and implementation so each initiative can move forward with clarity, accountability and purpose.
Design the Right AI Environment
Bring the workload, constraints and desired outcomes. We will start from the work—not the hardware.
Request an Infrastructure BriefingIn practice
Capability in context
These examples place the capability in context across real organizational environments.
Aligned to current buyer demand
AI infrastructure & compute
Plan GPU workstations, private AI systems and larger infrastructure around actual model, workload, storage and networking requirements.
AI hardware CanadaGPU workstationPrivate AI infrastructureOn-prem AI computeMedical imaging AI computeIndustrial AI workstation
These phrases are reflected as buyer needs and navigation cues—not repeated as keyword stuffing. The page is structured to answer the underlying decision behind the search.