AI workloads pushed the network to its limits. This post walks through the protocols, topologies, and failure modes that define modern AI networking — from RoCE and collective operations to fat-tree fabrics and packet-level telemetry.
How modern AI serving systems split a single model across many accelerators. Covers tensor, pipeline, and expert parallelism, KV-cache sharding, and production patterns from vLLM, TensorRT-LLM, and DeepSeek.
GPUs are no longer a hidden infrastructure cost. They are a tracked business metric, tied to revenue, margins, and team accountability. This post unpacks why utilization moved from engineering dashboards to board decks, and what it means for the way you build, schedule, and bill ML workloads.
Kubernetes has quietly become the de facto control plane for production AI inference. This post walks through the architecture, the GPU plumbing, and the patterns teams use to serve models that actually meet SLOs.
We follow a single agent request from the user prompt down through the AI gateway, inference server, GPU/Kubernetes scheduler, and east-west networking — then turn around and look at how observability, security, and FinOps wrap around the whole thing.