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
AI-powered firewall troubleshooting can transform how infrastructure teams investigate complex network security incidents. However, building a reliable troubleshooting platform requires more than simply adding an LLM to existing network data. This field report explains how I built an AI-assisted network troubleshooting pipeline using NSX-T, VRNI, Neo4j, vector search, reranking, Redis, machine learning, and LLMs. The
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
Enterprise private LLM infrastructure requires more than powerful GPUs. To achieve reliable AI performance, organizations must carefully design the compute, networking, storage, power, cooling, and physical infrastructure that support large-scale GPU workloads. Unlike traditional data center applications, AI workloads generate highly synchronized, high-bandwidth traffic between GPUs. As a result, GPU cluster networking, low-latency connectivity, congestion
Learn Kubernetes GPU scheduling with RuntimeClass, taints and tolerations, node affinity, topology and gang scheduling using NVIDIA GPU workloads. Part 11 — This post is part of the Falcon AI Workbook Series.. Four concepts have quietly appeared throughout every prior post without a full explanation: Runtime Class, taints/tolerations, node affinity/topology, and gang scheduling. This post
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:
