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Kubernetes

GPUs on Kubernetes: From Bare Metal to Schedulable in One Operator

A fresh Kubernetes cluster has no idea your nodes have GPUs. kubectl describe node shows CPU, memory, and pods — nothing else. To make a pod request a GPU you need a driver, a container runtime hook, and a device plugin advertising the hardware to the scheduler, all version-matched across every GPU node. Do it by hand and you'll re-do it on every kernel bump. This post wires it up the way you actually want — one operator — and works on any Kubernetes, not a specific vendor's distro.

Stacking MIG and Time-Slicing on One GPU Operator values.yaml

MIG carves a GPU into hardware-isolated slices. Time-slicing oversubscribes each slice so more pods can share it. Stack them and one physical GPU advertises far more schedulable units than it has silicon — useful when you have more workloads than GPUs and most of them sit idle. Here's the exact values.yaml, wired into the kommander-applications GPU Operator 26.3.0 app1, and the labels that switch a node between layouts.

Build Your Own Token-as-a-Service: A Self-Hosted OpenAI-Compatible AI Gateway on Kubernetes

Public LLM APIs bill you per token and leak your prompts off-prem. The fix isn't "run Ollama on a box" — it's a centralized, OpenAI-compatible inference platform your whole org consumes like a utility: one API key, token-based quotas, models that stay in your datacenter. Call it token-as-a-service. This post is the architecture — model tiering, GPU math, MIG partitioning, vLLM tuning, and KV-cache-aware routing — generic enough to run on any Kubernetes cluster with NVIDIA GPUs.

GPU DRA on Kubernetes: migrating off the NVIDIA device plugin, end-to-end

Dynamic Resource Allocation went GA in Kubernetes 1.351, and NVIDIA's DRA driver for GPUs shipped v0.4.1 on 2026-06-302. The nvidia.com/gpu extended resource model — the one the device plugin has used since 2017 — now has a sanctioned successor. The migration is non-trivial because the resource model is completely different (claim-based, not extended-resource-based), but the payoff is real: structured GPU allocation, native NVLink topology awareness, and a single API for MIG, MPS, time-slicing, and Multi-Node NVLink.

This post is the migration path: install the driver, write the first claim, swap one workload at a time, and run device plugin + DRA side-by-side until you're done.

Cilium and eBPF: the Networking Layer Under Your Cluster

Your cluster routes every packet through iptables, and at a few thousand Services that chain becomes a linear scan the kernel walks per connection. Cilium throws that out and runs networking, policy, and observability as eBPF programs in the kernel instead — no sidecars, no iptables, no agent in the data path. This post walks the whole stack: how a packet even finds another pod, installing Cilium as a kube-proxy replacement, watching real traffic with Hubble, locking the cluster down with identity-based policy, stretching it across clusters with Cluster Mesh, and dropping the service-mesh sidecars — all copy-paste.

Centralize Log Solution with the Elastic Stack

Use case: ship Kubernetes ingress logs off the cluster into a self-managed Elastic Stack on Ubuntu servers, parse them, and keep them searchable for 90 days without blowing the disk budget. The Elastic Stack handles this cleanly, and the 2026 build leans on four current features that older tutorials skip: LogsDB, ILM, data streams, and a Kubernetes-native shipper.

GPU Sharing on Kubernetes: Time-Slicing, MIG, and MPS Compared for LLM Workloads

You have 8 H100s. You have 30 inference pods that need one GPU each. The naive answer — buy 22 more H100s — is a quarter-million-dollar mistake. The right answer is GPU sharing, and there are four of them now: time-slicing, MIG, MPS, and DRA. Pick wrong and you get OOM kills, throughput collapse, or training jobs that silently corrupt each other. Pick right and you run 30 pods on 8 GPUs with measurable isolation guarantees.

This post walks each option with the actual config, the actual tradeoffs, and the decision tree that tells you which one to use for which LLM workload.

An On-Premise RAG Reference Architecture for 100 Users: Right-Sized GPUs, HA by Design

You can build a production RAG platform on your own hardware for the price of two years of API bills, and it fits in half a rack. The trick is sizing the GPUs to the models instead of buying the biggest card on the truck. This is the full design for a 100-user, highly available, hybrid-plus-graph RAG stack, with every GPU choice backed by the VRAM math.

Stateful ai workloads on kubernetes

Every RAG team hits the same wall six months in. They started with PostgreSQL and pgvector because it was the path of least resistance, the embeddings worked, the retrieval was fine, and nobody had to learn a new system. Then the vector count crossed some invisible threshold — usually around 10M–50M, depending on dimensionality — and recall started sliding, query latency started climbing, and the migration conversation began. The problem: migrating from pgvector to a dedicated vector database while you have live embeddings in production is brutal, and most teams either over-engineer (spinning up a Weaviate cluster for 10K vectors) or under-engineer (running pgvector into the ground at 50M).

This is the decision tree I wish someone had handed me. It's tuned for Kubernetes, June 2026, and every number is from a primary source.