The right compute architecture follows the model, data and operational targets.
Accelerated servers introduce constraints beyond general-purpose compute: accelerator memory, parallelism, interconnect bandwidth, storage throughput, power density and cooling.
Workload characterisation
Training, fine-tuning, inference and data processing behave differently. Sizing follows concurrent users, model footprint, target latency and batch constraints.
- —Memory, throughput and utilisation expectations
- —Scheduling and multi-tenancy requirements
- —Data sensitivity and residency expectations
Platform dependencies
Compute is matched with storage and network fabric so one layer does not starve another.
- —GPU-to-GPU and node interconnect considerations
- —High-performance storage feeding patterns
- —Rack, power, cooling and serviceability
Operating model
A deployment must support isolation, observability, upgrades and failure recovery.
- —Provisioning and driver lifecycle
- —Access control and workload quotas
- —Monitoring, backup and support ownership
How a KABO project would progress
Project plans are shaped around site access, dependencies, approved equipment and actual business constraints. This is an indicative engineering sequence, not a claim that a specific customer deployment has been completed.
Establish scope, accountabilities and acceptance criteria.
Translate discovery into measurable technical decisions.
Resolve compatibility and operational dependencies.
Execute in controlled, reversible stages.
Verify, document and transfer operational ownership.
What we would ask you first
- Target models and frameworks
- Data sizes and concurrency
- Available rack power and cooling
Final specifications, testing obligations, project pricing and service commitments require site-specific validation and signed scope.
Talk to KABO about GPU compute platforms