GPU Management: What's Different in 2025
The AI revolution has made GPUs critical infrastructure. But traditional DCIM platforms treat them like any other server component. That's a problem.
Why GPUs Are Different
A GPU isn't just a power-hungry component—it's a specialized compute resource with unique characteristics that demand specialized management:
- Dynamic Power Consumption: GPUs can swing from idle to peak power in milliseconds, creating power management challenges traditional DCIM can't handle
- Thermal Density: Modern AI accelerators generate extreme heat in small form factors, requiring precise cooling management
- Utilization Patterns: Unlike CPUs, GPU utilization directly correlates with business value, making real-time tracking critical
- Cost Per Unit: At $30K+ per GPU, accurate tracking and optimization has massive financial impact
The Traditional DCIM Approach
Legacy DCIM platforms like Nlyte and Sunbird track GPUs as line items in asset databases. You know where they are and when you bought them, but that's about it. Want to know:
- Which GPUs are actually being utilized right now?
- What workloads are running on specific GPUs?
- Which clusters have capacity for new jobs?
- How power usage correlates with actual compute output?
Traditional DCIM has no answers. You need custom integrations, manual spreadsheets, or entirely separate tools.
The Modern Approach
Aravolta treats GPUs as first-class citizens with native integration into NVIDIA's management stack, Kubernetes GPU operators, and ML orchestration platforms. This enables:
Real-Time GPU Intelligence
- 🎯Utilization Tracking
See actual GPU compute usage across all your clusters in real-time
- ⚡Power Efficiency
Correlate power consumption with actual workload value
- 🌡️Thermal Management
Monitor GPU temperatures and cooling efficiency per device
- 📊Capacity Planning
Predict when you'll need more GPU capacity based on usage trends
Integration with ML Workflows
Modern GPU management must integrate with your ML operations stack. Aravolta connects with Kubernetes, Ray, SLURM, and other orchestrators to provide end-to-end visibility from infrastructure to application:
- See which training jobs are running on which GPUs
- Identify underutilized resources and rebalance workloads
- Track cost per model training run
- Alert on anomalous power or thermal patterns
Cost Optimization
With GPUs representing 70-80% of modern AI infrastructure costs, optimization delivers massive savings. Organizations using Aravolta report:
- 25-40% improvement in GPU utilization rates
- 30% reduction in wasted capacity
- Better workload scheduling reducing job queue times
- Data-driven decisions on GPU purchases vs cloud bursting
The Future of GPU Management
As AI becomes more central to business operations, GPU infrastructure management will be a competitive advantage. Organizations that treat GPUs as fungible resources will overspend and underperform. Those that implement intelligent GPU management will maximize ROI on their most expensive infrastructure.
Getting Started
If you're running AI workloads on GPUs, modern DCIM with native GPU management isn't optional—it's essential. Aravolta's GPU management capabilities are included in all tiers, not sold as expensive add-ons.