Multi-Cloud GPU Orchestration for AI Workloads
An intelligent resource allocation fabric integrating bare-metal and cloud GPUs with Kubernetes-aware scheduling to improve utilization by 50%.
The Challenge
AI workloads required massive GPU resources but suffered from poor utilization, complex scheduling, and high costs. The client needed a unified platform to manage both bare-metal and cloud GPUs across multiple providers while optimizing resource allocation and reducing operational overhead.
Our Solution
We built an intelligent GPU orchestration platform for multi-cloud AI workloads
Kubernetes Integration
Developed custom Kubernetes scheduler with GPU-aware resource allocation and workload optimization for AI/ML tasks.
Multi-Cloud Management
Created unified abstraction layer for managing GPUs across AWS, GCP, Azure, and bare-metal infrastructure.
Intelligent Scheduling
Implemented AI-powered scheduling algorithms that optimize GPU utilization based on workload requirements and cost constraints.
HPC Integration
Built seamless integration with HPC schedulers like Slurm for hybrid workloads spanning traditional and AI computing.
Results & Impact
Technology Stack
Advanced orchestration and cloud technologies