AI-Ready On-Prem GPU Cluster with 90%+ Utilization
From Siloed Workstations to a High-Performance Distributed Fabric
The Challenge: Roadblocks to Scale
The client faced multiple infrastructure and operational challenges that limited AI adoption and ROI:
- Infrastructure Debt High OpEx from cloud-only scaling with poor long-term ROI.
- The “1Gbps Ceiling”Standard networking resulted in 96% GPU idle time during training.
- Siloed AccessManual workstation management prevented team collaboration and resource sharing.
The Solution: Phased Infrastructure Transformation
Phase 1: Intelligent Orchestration
- Consolidated isolated nodes into a unified, shared compute pool.
- Implemented automated resource scheduling and time-slicing.
- Standardized developer access through a centralized web-based IDE.
Phase 2: High-Speed Interconnect
- Deployed a dedicated, low-latency high-performance network.
- Enabled RDMA-based zero-copy transfers for direct inter-GPU communication.
Key Impact
| Metric | Before | After |
|---|---|---|
| GPU Utilization | < 5% | > 90% |
| Throughput | 1Gbps bottleneck | 100Gbps Fabric |
| Training Time | Days | Hours |
| Cloud Spend | High (Primary) | Reduced (Burst Only) |
| Scalability | Manual / Static | Plug-and-Play Expansion |
| TCO | OpEx: Cloud bills for POCs & Demos | CapEx: Pay only for production |