CASE STUDY
GPU Infrastructure Architecture for On-Prem AI Workloads
Established a scalable, high-performance compute environment capable of supporting enterprise-grade generative AI workloads entirely on-premise.
Situation
The client required significant GPU compute capacity while maintaining full control over infrastructure, data locality, and performance optimization.
Solution
Designed and deployed a hybrid GPU infrastructure.
OUTCOMES
$2.6M avoided
equivalent annual cloud spend
Localized AI
for sensitive workloads
3.4x higher
GPU utilization across mixed workloads
Challenges
Performance
- •High compute demand
- •Training latency constraints
Control
- •Data locality constraints
- •Infrastructure ownership constraints
Solutions
01
Hybrid GPU Deployment Strategy
Combined consumer and data center-grade GPUs for cost-performance optimization.
- Balanced cost and performance across hardware tiers
- Leveraged heterogeneous GPU clusters efficiently
- Optimized resource allocation per workload
02
Multi-Node Cluster Architecture
Configured multi-node compute clusters within a controlled data center environment.
- Built scalable distributed training environments
- Improved infrastructure resiliency
03
Training/Inference Optimization
Optimized workloads for training and inference efficiency.
- Reduced inference latency across models
- Increased utilization of GPU resources
04
High-Throughput Data Integration
Integrated storage and networking to support high-throughput data pipelines.
- Connected storage systems for rapid dataset access
- Optimized network bandwidth across nodes
- Supported large-scale model training workflows