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 exotic, but scheduling them well is still hard. This post walks through the policies, primitives, and production patterns that decide who gets the silicon.
AI infrastructure is no longer a subfield of MLOps — it is the convergence of six traditional disciplines. Here is how Terraform, Kubernetes, GPU scheduling, inference routing, SRE, and FinOps are collapsing into a single operating model.
AI infrastructure hiring is converging into a single, hybrid discipline that blends platform engineering, MLOps, and distributed systems. This post breaks down what skills are merging, what the new job descriptions look like, and how engineers can position themselves.