Demystifying VeloCloud: A Comprehensive Guide to Licensing, Support and BOQ As enterprises move away from rigid legacy WAN architectures, SD-WAN has become an important part of modern network transformation. VeloCloud SD-WAN provides organizations with a flexible way to connect branches, data centers, cloud environments, and remote locations while improving application performance and network visibility. However,
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 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.
Building a Kubernetes AI Platform on NVIDIA GPUs Up to this point, the Falcon AI workbook has focused primarily on infrastructure. We deployed the NVIDIA GPU Operator, validated drivers, tested GPU resources, and confirmed that real workloads can run successfully across the cluster. However, a validated GPU cluster is not yet a complete Kubernetes AI
