Enabling AI Compute at Scale
How we helped an AI startup achieve 3x faster training times while reducing costs by 30%
Client Overview
The Challenge
Our client, a cutting-edge AI startup, needed to build a real-time LLM platform that required significant computational resources. They faced challenges with accessing high-performance GPUs and maintaining zero downtime while scaling their operations.
The Solution
We implemented a sophisticated 8-node GPU cluster powered by NVIDIA H100s, complemented by 1PB of DAOS-backed storage. This architecture was designed to handle intensive computational workloads while ensuring optimal performance.
Key Achievements
Training Speed
3x Faster
Significantly reduced model training times through optimized GPU utilization and parallel processing
Cost Reduction
30% Lower
Decreased monthly operational costs through efficient resource allocation and management
User Capacity
10x Growth
Enabled the platform to handle 10 times more concurrent users without performance degradation
Technical Implementation
Infrastructure
- • 8-node GPU cluster deployment
- • 16x NVIDIA H100 GPUs
- • High-availability configuration
- • Load balancing optimization
Storage Solution
- • 1PB DAOS-backed storage
- • High-speed data access
- • Redundant backup systems
- • Automated data replication
Processing Power
- • Distributed computing setup
- • Real-time processing capability
- • Dynamic resource allocation
- • Automated scaling