Most people think Kubernetes can automatically use GPUs.It can’t.
Unlike CPUs and memory, Kubernetes has no built-in understanding of GPU hardware. Before an AI workload can use an NVIDIA GPU, several components must be installed to expose and manage that hardware.
In today’s video, we explore how Kubernetes actually gains GPU support.
We cover:
✅ Why Kubernetes cannot use GPUs by default
✅ GPU vendors supported by Kubernetes (NVIDIA, AMD, and Intel)
✅ What Device Plugins are and why they’re required
✅ How the NVIDIA GPU Operator simplifies GPU management
If you’re a DevOps Engineer, Platform Engineer, SRE, or Kubernetes Administrator, understanding GPU integration is one of the first steps toward running LLMs and Generative AI workloads in production.
🎥 Watch the video here:
🔗 Day 15 (English): https://www.ideaweaver.ai/courses/100-days-of-genai-for-devops-english/lectures/66388346
🔗 Day 15(Hindi): https://www.ideaweaver.ai/courses/100-days-of-genai-for-devops-hindi/lectures/66388347
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