Network Bachelor

Explore private LLM architecture, GPU sizing, NVIDIA infrastructure, Arista networking, and secure enterprise AI deployment strategies.

Explore NVIDIA switch models, NVUE CLI commands, and supported networking protocols. Learn key features, configurations, and data center use cases.

Explore enterprise private LLM infrastructure architecture, GPU sizing, high-performance networking, security, and deployment best practices for AI workloads.

Master NVIDIA GPU workloads on Kubernetes in Part 11. Explore GPU scheduling, resource management, and key concepts to optimize AI/ML performance.

Compare NVIDIA MIG vs time-slicing on Kubernetes and learn which GPU sharing mode fits inference, notebooks, testing, and AI workloads.

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.

Explore NVIDIA GPU workloads on Kubernetes in Part 8. Learn to build scalable AI platforms, deploy applications, and optimize GPU-powered AI/ML workloads.

Learn how to validate NVIDIA GPU workloads on Kubernetes using CUDA VectorAdd, PyTorch, NCCL, gpu-burn and vLLM inference tests.

Validate an NVIDIA GPU Kubernetes cluster before running AI workloads. Check GPU health, RDMA networking, storage, scheduling, resources and observability.

Learn what runs on an NVIDIA GPU node in Kubernetes, from GPU drivers and container toolkit to device plugins, DCGM monitoring, and troubleshooting.