Network Bachelor

A network engineer’s field guide to the physics, topology, and QoS discipline behind modern GPU clusters — with Nexus config that actually runs. 0. Why This Post Exists Almost every “AI networking” article on the internet stops at “you need low latency and high bandwidth.” That is the equivalent of telling a CCIE candidate that

Part 11 — the closing post of the Falcon AI workbook series. Four concepts have quietly appeared throughout every prior post without a full explanation: RuntimeClass, taints/tolerations, node affinity/topology, and gang scheduling. This post gives each one the depth it deserves, so the series is a complete reference, not just a build log. Prerequisite You’ve

Part 10 of the Falcon AI workbook series. Every post so far treated each H100 as a single, whole unit of scheduling. This post changes that assumption — and uses the observability stack from Part 9 to actually show the difference rather than just describe it. Prerequisite You’ve completed Part 9: Prometheus, Grafana, and DCGM

Part 9 of the Falcon AI workbook series. Parts 5 and 6 confirmed DCGM Exporter and Node Exporter pods were Running — but “Running” isn’t the same as “wired into a dashboard someone actually looks at.” This post closes that gap: full-stack observability for cluster, nodes, GPUs, network, and the application layer from Part 8.

Part 8 of the Falcon AI workbook series. Everything through Part 7 was infrastructure — DaemonSets, drivers, validation. This post is where the cluster stops being “a bunch of GPUs” and becomes a self-service platform Falcon AI’s ML engineers can actually use without ever touching kubeadm. Prerequisite You’ve completed Part 7: all five test-job rungs

Part 7 of the Falcon AI workbook series. The cluster passed every category in Part 6’s checklist. Now we prove it end to end by actually running workloads — climbing a five-rung ladder from “can a container see a GPU at all” to “can this cluster serve a real LLM.” Prerequisite You’ve completed Part 6:

Part 6 of the Falcon AI workbook series. Everything is installed — control plane, operators, two GPU nodes, all four layers of the node stack. This post is the deep validation pass you run before anyone is allowed to submit a training job: six categories, one command block each, pass/fail criteria for every one. Prerequisite

Part 5 of the Falcon AI workbook series. gpu-wk-01 and gpu-wk-02 are up with nvidia.com/gpu: 8 Allocatable. Before we validate the cluster (Part 6) or run a workload (Part 7), let’s understand exactly what’s running on these nodes and why each piece exists — this is the knowledge that turns “it’s broken” into “I know

Part 4 of the Falcon AI workbook series. This is the payoff post — gpu-wk-01 and gpu-wk-02 join the cluster for real, and you’ll watch the entire reconcile chain from Part 3 fire in front of you. Prerequisite You’ve completed Part 3: GPU Operator and Network Operator are confirmed healthy and idle-watching, CRDs and NFD

Part 3 of the Falcon AI workbook series. Follow along with real commands — this post has no new infrastructure to build. Instead we go hands-on inside the operators installed in Part 2, so you know exactly what “idle and watching” actually means before we hand it real GPU hardware in Part 4. Prerequisite You’ve