A Comprehensive Technical Deep-Dive into Passive vs. Inline Decryption, Modern TLS 1.3 Challenges,Offloading ROI, and Security Tool Acceleration. Introduction to Enterprise TLS Visibility Enterprise networks carry more encrypted traffic than ever. As a result, security teams face a difficult visibility challenge. Encryption protects data integrity and user privacy. However, it can also hide malware, command-and-control
A field report from building an AI pipeline on top of NSX-T, VRNI, and Neo4j — for infrastructure engineers who thinkRAG and vector search are still buzzwords for someone else’s problem. The Problem Nobody Warns You About If you’ve run a firewall estate at any real scale, you know the drill. A ticket comes in:
As data sovereignty, regulatory compliance (such as GDPR, HIPAA, and financial frameworks), and intellectual property protection take center stage, enterprises are shifting rapidly from public cloud APIs to private Large Language Model (LLM) deployments. Operating a private LLM means your sensitive corporate data never leaves your infrastructure perimeter. However, building an internal LLM stack is
Explore NVIDIA switches, including Spectrum and Quantum models, NVUE CLI, protocol support, RoCE, InfiniBand, and AI networking. 0. Who This Is For If you’ve spent your career in configure terminal, write memory, and show ip route, and you’re now being handed a rack of NVIDIA switches that boot into a Linux prompt and expect you
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:
