Blog
Understanding AI infrastructure and Kubernetes from first principles.
Deep-dive articles on Kubernetes, GPU infrastructure, and AI platform engineering — written by a practitioner.
Why Kubeflow Trainer Exists: The Journey from Single GPU Training to Distributed AI on Kubernetes
Understand the evolution from single GPU training to PyTorch DDP and discover why Kubernetes needs Kubeflow Trainer to orchestrate distributed AI workloads.
How One Expired Certificate Quietly Stopped an Entire Kubernetes Control Plane
A real production incident that explains how one expired API server certificate silently stopped Kubernetes reconciliation and brought an entire control plane to a halt.
Scaling LLM Inference on Kubernetes: Why Your Pods Take 30 Minutes to Start (And How to Fix It)
# Scaling LLM Inference on Kubernetes: Why Your Pods Take 30 Minutes to Start (And How to Fix It)
Understanding How Kubernetes Supports GPUs
Learn how Kubernetes discovers, schedules and exposes GPUs to containers, and why components like the Device Plugin and GPU Operator are essential for running AI workloads.
GPU Architecture Explained: How Modern GPUs Execute AI Workloads
A practical guide to modern GPU architecture, explaining how Streaming Multiprocessors, CUDA Cores and Tensor Cores work together to execute AI workloads.
Understanding the GPU Memory Hierarchy
Explore the GPU memory hierarchy, from registers to HBM, and learn how each level of memory impacts GPU performance.
Why Is LLM Decoding Memory-Bound?
Understand why LLM decoding is memory-bound, how memory bandwidth limits token generation, and why Tensor Cores spend most of their time waiting for data.
How GPUs Execute Work: Understanding Threads, Warps, Thread Blocks and Grids
A practical explanation of how GPUs organise and execute work using threads, warps, thread blocks and grids.