AI Platform Operations
YOUR GPUs ARE IDLE.
YOUR TEAMS ARE WAITING.
Expensive silicon is a liability when it sits unallocated. Master Kubernetes GPU scheduling to eliminate queue chaos, improve utilization, and enforce fair multi-team access.
The "Stranded Compute" Crisis
Most enterprises running AI workloads on Kubernetes face a harsh reality: their GPUs are often active less than 20% of the time (varies by workload mix). The rest is wasted on "fragmented capacity"(resources scattered across nodes)—where GPUs are reserved but not utilized, or jobs are stuck in endless queues because the scheduler cannot see the available gaps.
Improving GPU utilization isn't just a technical task; it's a financial imperative. Without advanced GPU resource allocation, you are essentially paying for high-end silicon that isn’t producing business value.
The Hogging Problem
One research team requests 8 GPUs for a notebook and leaves them idle for 12 hours, starving production training.
Queue Deadlock
Standard Kubernetes schedulers lack "fair-share" logic, leading to first-come-first-served chaos and job starvation.
What "Good" Looks Like:
Predictable Throughput
Jobs start within defined SLA windows based on priority tiers, not luck.
80%+ Median Utilization
Aggressive bin-packing and time-slicing ensure every GPU clock cycle is monetized.
Multi-Tenant Governance
Hard quotas and soft limits prevent resource monopolies across business units.
The Kubernetes GPU Enablement Stack
Standard Kubernetes doesn't speak "GPU" natively. We implement the full GPU orchestration tools layer for production readiness.
Foundation
GPU Operator & Drivers Automated management of the NVIDIA software stack. We ensure the kubernetes GPU operator handles driver lifecycle, toolkit installation, and health monitoring without manual intervention. Device Plugin · GDS (Direct Storage) · Fabric Manager
Optimization
Resource Allocation Moving beyond integer-based allocation. We implement multi-tenant GPU patterns like Time-Slicing and Multi-Instance GPU (MIG) for fractional resource sharing. NVIDIA MIG · MPS (Process Sharing) · Node Pools “Approach depends on GPU model and workload isolation requirements.”
Governance
Scheduling Policy The brain of the operation. We design quota based GPU scheduling that understands namespaces, taints, and tolerations for fair-share workload placement. Gang Scheduling · Bin Packing · Preemption
Foundation
Tap to flip
GPU Operator & Drivers Automated management of the NVIDIA software stack. We ensure the kubernetes GPU operator handles driver lifecycle, toolkit installation, and health monitoring without manual intervention. Device Plugin · GDS (Direct Storage) · Fabric Manager
Optimization
Tap to flip
Resource Allocation Moving beyond integer-based allocation. We implement multi-tenant GPU patterns like Time-Slicing and Multi-Instance GPU (MIG) for fractional resource sharing. NVIDIA MIG · MPS (Process Sharing) · Node Pools “Approach depends on GPU model and workload isolation requirements.”
Governance
Tap to flip
Scheduling Policy The brain of the operation. We design quota based GPU scheduling that understands namespaces, taints, and tolerations for fair-share workload placement. Gang Scheduling · Bin Packing · Preemption
Beyond Standard Kubernetes Scheduling
Many teams look for a run ai alternative when they realize basic Kubernetes cannot handle the nuance of GPU preemption, fair-share, or automated job suspension. We provide a vendor-neutral evaluation and implementation of the orchestration layer that fits your specific TCO.
Evaluation Checklist:
- Does the platform support dynamic rescheduling of low-priority dev workloads?
- Can you implement "Showback" and "Chargeback" at a per-project level?
- Does it integrate with your existing MLOps stack (Kubeflow, MLflow)?
- Are the licenses creating a "vendor lock-in" that outweighs GPU savings?
GOAL: 3X THROUGHPUT PER NODE - Visibility is Optimization
GPU Utilization %: 88%
Queue Latency Reduction: -70%
"Stop measuring just 'uptime.' Start measuring 'Effective Token Generation per Watt.'"
How DataCouch Helps
We don't just recommend tools; we build capability within your platform team to maintain peak efficiency.
Audit
Baseline metrics on utilization, queue time, and failed jobs.
Blueprint
Policy design for quotas, priorities, and isolation.
Build Capability
Operator enablement and platform hardening.
Tune
Continuous feedback loops and policy refinement.
Labs
Hands-on training for MLOps and Dev teams.
Scheduling Deep Dive
Adding hardware often highlights underlying GPU scheduling bottlenecks. Low utilization is typically caused by "fragmented resources"—where the scheduler cannot fit a job because resources are scattered across nodes—or because jobs are reserved but idle during data loading/preprocessing. We address this via better GPU utilization optimization like bin-packing and using fractional GPU strategies.
We implement a multi-tenant GPU model using Kubernetes Namespaces combined with ResourceQuotas. However, simple quotas aren't enough for AI. We layer in "Priority Classes" and "Fair-Share" scheduling, ensuring that if Team A isn't using their allocated capacity, Team B can "borrow" it, but Team A can "reclaim" it immediately via preemption when needed.
Vanilla Kubernetes is designed for CPU-based web microservices. It lacks native support for GPU resource allocation concepts like fractional sharing (MIG), gang scheduling (all GPUs or none), and advanced preemption. While you can build this with open-source plugins (like Volcano or Kueue), larger enterprises often benefit from an orchestration layer (like a run ai alternative) to handle governance and visibility at scale.
We focus on four key metrics: 1) Median GPU Duty Cycle % (how often the silicon is actually computing), 2) Average Queue Wait Time (from job submission to start), 3) Stranded Resource Rate (idle GPUs during active job runs), and 4) Cost per Job/Experiment. Tracking these weekly allows you to prove ROI on your infrastructure spend.
Stop Wasting Silicon.
Talk to a platform expert about Kubernetes GPU scheduling, fair-share governance, and utilization optimization today.