How GPULoom supports multi-brand hardware sourcing, configuration review, export preparation and deployment planning.
Transparency note: The scenarios below describe representative engagement types, not named customer claims. Verified customer names, project values, photographs and delivery records will only be published with authorization.
AI / GPU Infrastructure
Typical need: multi-GPU compute nodes, high-speed fabric and rack-level power planning.
Our workflow: validate workload, GPU topology, host configuration, networking, power, cooling and delivery scope.
Virtualization Refresh
Typical need: replace aging compute while preserving hypervisor and storage compatibility.
Our workflow: normalize server BOMs, check memory population, NICs, rails, support and migration constraints.
Storage Expansion
Typical need: add usable capacity or performance without an avoidable platform mismatch.
Our workflow: verify controller, software release, shelves, drives, protocols, optics and spare policy.
Backup & Data Protection
Typical need: improve recovery, retention and cyber-resilience workflows.
Our workflow: review capacity, backup window, replication, retention, interfaces and support requirements.
Platforms used in these projects
- Rack and GPU servers – Dell PowerEdge, Lenovo ThinkSystem, Inspur and xFusion platforms
- Enterprise storage – PowerStore, PowerMax and Unity all-flash and hybrid arrays
- Data center GPUs – NVIDIA H100, H200, A100 and AMD Instinct accelerators
- Data center networking – ToR, aggregation and Fibre Channel switches
Start from the model or part number: product search.
What a verified case study will include
- Customer industry and approved region disclosure
- Business challenge and acceptance criteria
- Verified hardware models and delivery scope
- Configuration or deployment photographs approved for publication
- Outcome metrics supplied or approved by the customer