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How Canva Scales and Optimizes AI Workloads with Karpenter

What this session is about

his session explores how Canva leverages Karpenter to scale and optimize diverse workloads on Amazon EKS. Learn how Canva manages AI workloads using On-Demand Capacity Reservations (ODCRs) and EC2 Capacity Blocks for ML, while maximizing resource utilization by intelligently co-locating CPU and GPU workloads on GPU nodes. We will dive into NodePool management strategies for efficient scheduling of AI workloads and examine how Canva uses a range of Amazon EC2 instance types to operate a multi-tenant container orchestration platform for all workloads, optimizing for cost-effectiveness and resource efficiency. Ideal for platform engineers and Kubernetes operators looking to optimize their EKS clusters for both AI and general workloads at scale.

Playbook

Editorial commentary · what to actually do about this on Monday

The concept
Karpenter for diverse EKS workloads. On-Demand Capacity Reservations + EC2 Capacity Blocks for ML. CPU + GPU workload co-location on GPU nodes. NodePool strategies for AI scheduling.
Why it matters
AI workload scheduling on EKS is operationally complex; getting it wrong wastes money fast.
The hard parts
GPU node utilisation is hard to maximise. Idle GPUs are very expensive.
Playbook moves
(1) Co-locate batch CPU work on GPU nodes. (2) Let Karpenter handle node lifecycle. (3) Monitor GPU utilisation as carefully as you monitor CPU.
The surprise
GPU co-location with CPU workloads can hit 70%+ GPU utilisation on otherwise-idle hardware. That's the cost-saving most teams miss because they segregate GPU and CPU workloads "for clarity." Co-location pays back the operational complexity many times over. ---

Independent editorial perspective — not an official AWS or speaker statement. Designed for executives evaluating what to brief their teams on next.

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