GPU Allocation Policies for Kubernetes

Problem Context

Teams running AI/ML workloads on Kubernetes struggle to manage GPU resources efficiently. They mix inference workloads with regular services, but GPUs get starved, manual scaling is error-prone, and native tools like Karpenter or NVIDIA’s device plugin don’t enforce priorities or sharing rules. Without automation, engineers waste hours fixing allocation issues, and businesses lose revenue from downtime or inefficient resource use.

Pain Points

Users try Karpenter or Cluster Autoscaler but hit pain points like slow provisioning or no GPU sharing. MIG/time-slicing is manual and inconsistent. K8s’ Dynamic Resource Allocation (DRA) APIs are too new and lack prioritization. Manual ‘duct-tape’ solutions (e.g., scripts, consultants) create technical debt and don’t scale. Without clear policies, CPU workloads often hog GPUs, breaking inference services.

Impact

GPU starvation causes inference API failures, costing thousands per hour in lost revenue. Manual scaling wastes 5+ hours/week per engineer. Over-provisioning GPUs inflates cloud bills by 20–30%. Teams lack visibility into who/what is using GPUs, leading to blame games and inefficiencies. Downtime during peak hours (e.g., e-commerce inference) directly impacts customer conversions.

Urgency

This is urgent because GPU costs are a major line item in cloud bills (e.g., $10k/month for EKS + GPUs). Without automation, teams either over-provision (wasting money) or under-provision (risking downtime). The problem worsens as AI workloads grow, making it a blocking issue for scaling. Engineers can’t ignore it—every outage or misallocation is felt immediately in production.

Target Audience

DevOps/SRE engineers at mid-size tech companies (50–500 employees) running Kubernetes clusters with GPUs. Also affects MLOps engineers, data science teams, and cloud architects at AI startups or enterprise ML teams. Companies using EKS, GKE, or AKS for inference or training workloads face this daily. Even large enterprises struggle with GPU sharing across teams (e.g., dev vs. prod).

Solution Approach

A lightweight Kubernetes operator that enforces GPU allocation policies automatically. It acts as a ‘traffic cop’ for GPUs, ensuring inference workloads get priority, dev teams don’t hog resources, and no workload starves. Users define policies (e.g., ‘inference gets 70% GPUs, dev gets 20% after 6 PM’) via a simple YAML file or dashboard. The tool then adjusts allocations in real-time, sends alerts for violations, and provides visibility into usage.

Key Features

1. Automated Sharing: Supports MIG (NVIDIA’s multi-instance GPU) and time-slicing, but with smarter defaults (e.g., ‘never let CPU workloads use >40% of a GPU’).
2. Real-Time Dashboard: Shows GPU usage by workload, policy violations, and historical trends (e.g., ‘Your training job used 90% GPUs for 3 hours—here’s who else was affected’).
3. Alerts: Slack/PagerDuty notifications for breaches (e.g., ‘CPU pod is violating GPU limits—auto-scaling it down’).

User Experience

Users install the operator via Helm in 5 minutes. They set policies in a web UI or YAML (e.g., ‘team=A gets 60% GPUs, team=B gets 40%’). The tool then runs in the background, adjusting allocations and sending alerts. Engineers see a dashboard showing GPU usage by team/workload, with clear visuals for violations. No more manual scripting or guessing—policies are enforced automatically, and alerts surface issues before they cause downtime.

Differentiation

Unlike Karpenter (which only scales nodes) or NVIDIA’s device plugin (which lacks prioritization), this tool combines policy enforcement, automated sharing, and visibility. It’s lighter than enterprise monitoring tools (e.g., Datadog) but more powerful than manual scripts. Policies are customizable (e.g., ‘weekend rules for dev teams’) and enforceable, unlike K8s DRA APIs. The dashboard gives teams the visibility they lack in native tools, and alerts prevent fires before they start.

Scalability

Starts with a single cluster but scales to multi-cluster setups via a central dashboard. Policies can be templated for consistency across environments (e.g., ‘prod policies’ vs. ‘dev policies’). Pricing scales with team size (e.g., $49/month for 10 users, $99/month for unlimited). Add-ons like Slack integration or custom policy templates unlock more value as teams grow. Enterprises can white-label the dashboard for their internal teams.

Expected Impact

Teams reduce GPU waste by 20–30% (saving $2k–$5k/month in cloud costs). Downtime from starvation drops to near-zero, protecting revenue from inference APIs. Engineers save 5+ hours/week on manual scaling and troubleshooting. Managers get visibility into GPU usage by team, enabling fairer resource allocation. The tool pays for itself in <1 month for most teams, making it a no-brainer for DevOps budgets.