Automated AI / GPU Resource Optimization for Kubernetes and Cloud
Continuously optimize GPU infrastructure to reduce cost, increase utilization, and eliminate manual tuning—at scale.
SRE’s, Platform Owners and FinOps teams
are succeeding and saving $millions.
Capabilities and benefits
Servicing your AI workloads could be more expensive than it needs to be – we can show you why
Visibility and Insight
Gain real-time and granular understanding of GPU usage and efficiency:
- Understand the utilization of every AI workload at every level of the stack
- Reclaim wasted capacity by evicting misplaced pods
- Recover idle or forgotten GPUs
- Real-time, predictive and adaptive insights: Continuously evaluate needs using AI-driven analytics.
GPU Resource Optimization
Place workloads on the right GPU, at the right time:
- Dynamically partition and assign GPUs to match demand
- Ensure containers use only what they need—no more, no less
- Maximize throughput by co-locating compatible jobs
- Protect high-priority jobs from being compromised or impacted
Efficiency and Cost Optimization
Maximizes GPU yield while minimizing spend:
- Slash GPU infrastructure cost through precision optimization
- 50% higher utilization, 50% cost reduction
- Automated resource decisions eliminate human guesswork and intervention
The result:
-
60 %
Higher GPU utilization
-
50 %
Cost savings
-
0
Zero guesswork
See the benefits of optimized
Kubernetes Resources
AI-driven analytics that precisely determine optimal resource settings for Kubernetes.
FAQs
Video
Datasheet
Podcast
