CAPABILITY / COMPUTE ARCHITECTURE
Put each workload in the right place.
Turn training, simulation, evaluation and inference requirements into a deployment topology with clear assumptions.
Discuss your deployment
Concrete project outputs.
Workload map
Identify resource demand, scheduling needs, dataset access and dependencies.
Placement decisions
Compare cloud, on-premises and edge placement against latency, locality and operating constraints.
Sizing assumptions
Record what is measured, what is estimated and which workload tests are still needed.
Deployment topology
Document resource pools, network boundaries, storage and ownership.
How the integration is delivered.
01
Profile
Gather representative jobs, model sizes and concurrency requirements.
02
Map
Trace compute, storage and data-transfer dependencies.
03
Evaluate
Review placement options against measured constraints.
04
Specify
Deliver topology, assumptions and a validation plan.
What we need to assess the scope.
Existing environment
Current compute, storage, network and access boundaries.
Representative workloads
Development, simulation or evaluation tasks and expected use patterns.
Project constraints
Data locality, availability needs, budget boundaries and procurement dependencies.
Define acceptance before implementation.
Deliverables and checks are agreed for the project environment. Dependencies, unmeasured assumptions and third-party constraints remain visible in the scope.
The handover should identify tested behavior, known limits and the next operating action. Published examples here describe a service approach, not a guarantee for every device or deployment.
Questions to resolve early.
Does this include supplying GPU hardware?
Hardware supply or capacity reservation is not implied by architecture work. Those requirements must be scoped and commercially confirmed separately.
Can an exact GPU count be given without workload evidence?
Sizing begins with representative workloads and concurrency assumptions. Unmeasured estimates are identified as assumptions rather than guarantees.
Discuss compute architecture.
Share your current stack and the boundary you need to connect.
Start a deployment discussion