AI server architecture
Size training, fine-tuning and inference infrastructure around model memory, concurrency and growth. Evaluate GPU platforms, CPU balance and accelerator interconnects.
Workload sizing & platform recommendations

NVIDIA HGX™, NVIDIA MGX™ and rack server platforms, backed by workload-led technical consulting for AI compute, private cloud and data-centre setup planning.
From a single GPU server to a planned enterprise compute environment, SG PR INFOTECH LLP helps translate business and AI workloads into a practical infrastructure plan.
Bring compute, storage, networking and facility readiness into one technical conversation—before committing to equipment.
Discuss your infrastructure
Size training, fine-tuning and inference infrastructure around model memory, concurrency and growth. Evaluate GPU platforms, CPU balance and accelerator interconnects.
Workload sizing & platform recommendations
Plan compute for business applications, virtualisation and private-cloud environments, with capacity headroom, hardware compatibility and migration dependencies in view.
Compute architecture & capacity plan
Match storage capacity, throughput and latency to datasets and applications. Plan NVMe tiers, shared storage, backup retention and recovery requirements.
Storage sizing & protection strategy
Evaluate Ethernet or InfiniBand for GPU clusters, alongside switching, uplinks, network segmentation and separate management connectivity.
Fabric selection & connectivity plan
Assess rack layouts, power budgets, UPS and PDU needs, cooling approach and cabling. Coordinate site-readiness requirements with facility and implementation specialists.
Rack, power & cooling readiness checklist
Define integration, acceptance testing, monitoring, access controls and handover requirements, with backup and recovery objectives agreed before deployment.
Validation plan & operational handover scope
Capture applications, model sizes, users, data volumes, budget and site constraints.
Compare platforms, size the infrastructure and identify facility dependencies.
Agree the bill of materials, implementation responsibilities and acceptance criteria.
Coordinate workload testing, readiness checks and documentation for handover.
Consulting deliverables, installation responsibilities, software licensing and support terms are agreed per project. Performance, availability and facility certification require project-specific validation.
High-density GPU systems configured around the platform specifications supplied for demanding data-centre workloads.



Dense storage, PCIe Gen5 expansion and redundant power options for networking, hybrid cloud, private cloud and GPU-enabled compute.


Final configurations depend on application, model size, throughput, storage, network fabric, rack environment and deployment requirements.
CPU generation, GPU platform, accelerator count and memory profile.
NVMe, SATA or SAS media, hot-swap bay count and resilience.
Ethernet or InfiniBand throughput, DPU and NIC requirements.
Power, rack space, cooling, redundancy and platform certification needs.
Share your workloads, model sizes, capacity targets and site-readiness needs.