Architecting GPU Platforms on Kubernetes
Learn why GPU sharing is fundamentally different from CPU, how to architect for security and performance, and which patterns actually work in real-world multi-tenant environments.
Kubernetes wasn’t built for GPUs, but your AI workloads demand them.
GPUs are costly, hard to isolate, and tricky to share in Kubernetes. This technical series gives platform leaders the strategies and architectures they need to build secure, scalable, GPU-enabled infrastructure for AI.
GPU-enabled Platforms on Kubernetes
GPUs resist Kubernetes' containerization model—they cannot be overcommitted, lack isolation mechanisms, and introduce security vulnerabilities when shared. This technical manual explains how Kubernetes abstracts GPU resources, why traditional isolation fails, and what architectural patterns enable multi-tenant GPU platforms. Platform engineers will learn the mechanics from device plugins to Dynamic Resource Allocation, understand security implications from side-channel attacks to memory persistence, and discover production-tested approaches ranging from simple time-slicing to VM-based isolation.
GPU Enabled Platforms Overview
GPUs are expensive, isolation is tricky, and Kubernetes doesn’t make it easy. This technical series is built for platform engineering leaders designing infrastructure for AI at scale. Learn why GPU sharing is fundamentally different from CPU, how to architect for security and performance, and which patterns actually work in real-world multi-tenant environments.
Multi-Tenancy Fundamentals: Why GPU Sharing is Harder in Kubernetes
This session exposes the fundamental mismatch between Kubernetes' resource model and GPU hardware constraints. You'll discover why the absence of GPU memory cgroups, non-preemptible GPU kernels, and persistent memory states create not just performance bottlenecks but serious security vulnerabilities.
Multi-Tenant GPU Platforms: Reference Architectures
In this webinar, you'll explore proven architectures for building multi-tenant GPU platforms that maximize utilization while meeting diverse workload requirements. Drawing from real-world implementations, we'll examine how to classify workloads by trust level and performance characteristics and map them to appropriate isolation mechanisms—from namespace separation for high-trust teams to hardware-enforced MIG isolation for production inference.
Understanding GPU Resources in Kubernetes
Join us for a deep technical exploration of GPU resource management in Kubernetes, where we'll dissect the complete architecture stack from physical hardware to container execution. This webinar unveils the hidden complexity behind what appears to be a simple resource request, revealing why GPU scheduling presents unique challenges and how Kubernetes orchestrates the intricate dance between device plugins, schedulers, and container runtimes.
GPU Sharing Mechanisms in Kubernetes
Are you struggling to maximize GPU utilization while meeting diverse workload requirements? As GPU costs soar and demand grows, choosing the right sharing strategy can make or break your infrastructure efficiency.
Get early access to achitecting GPU platforms on Kubernetes.