GPU Resource Scheduler for Multi-Team AI Organizations
Organizations with shared GPU clusters waste 30-40% of capacity due to poor scheduling. A Kubernetes-native GPU scheduler with fairshare policies, topology-aware placement, and cost attribution could maximize expensive GPU utilization.
Problem Statement
Multiple teams in an organization compete for shared GPU resources. Standard Kubernetes scheduling doesn't understand GPU topology, multi-node training requirements, or fairshare policies. This leads to job failures, wasted GPU hours, team conflicts over resource allocation, and inability to attribute costs accurately.
The Idea
A managed GPU scheduling platform that sits on top of Kubernetes to optimize multi-team GPU allocation with fairshare policies, topology-aware placement, queue management, and per-team cost attribution.
Why Now
NVIDIA's KAI Scheduler (1.2K stars) and the explosion of GPU demand demonstrate that standard Kubernetes scheduling is insufficient for AI workloads. Organizations are spending $100K-1M+/month on GPUs but utilization averages only 40-60% due to scheduling inefficiencies that standard tools cannot solve.
Target User
ML platform engineers and infrastructure leads managing shared GPU clusters for multiple AI teams
Target Market
Organizations with 50+ GPUs shared across 3+ teams running diverse AI workloads
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