Infrastructure & Runtime

Where AI models run determines everything about latency, cost, privacy, and operational risk. This cluster covers the runtime and serving layer: inference optimization, hardware tradeoffs, RAG architectures, vector search at scale, and the growing ecosystem of edge and on-device deployment.

Serving-side architecture has undergone a genuine paradigm shift with prefill-decode disaggregation. The insight—that prefill is compute-bound while decode is memory-bandwidth-bound, and routing them to different hardware pools eliminates the phase interference that inflates P99 latency—is now being productized by NVIDIA Dynamo, vLLM’s disaggregated serving, and Mooncake at ByteDance scale. If you’re running inference at volume, this architectural decision is no longer academic.

The local and edge stack has consolidated around a shorter list of serious runtimes. MLX performs significantly better than llama.cpp on Apple Silicon for sub-14B models while llama.cpp remains the right call for cross-platform deployments and long contexts. Google’s LiteRT (successor to TensorFlow Lite) anchors the Android/embedded side. The tradeoffs are measurable; Groundy publishes benchmark results rather than vendor summaries.

RAG production architecture is where theory consistently meets deployment reality. The gap between a notebook demo and a system that handles document poisoning, retrieval precision degradation under index growth, and embedding drift over time is where most RAG projects stall. Groundy covers the failure modes—not just the happy-path architecture diagrams.

Hardware selection is increasingly a first-class decision. Microsoft’s BitNet 1-bit quantization, NVIDIA’s open-source quantum-calibration models, and Alibaba’s ZVEC vector database each represent architectural bets on where the cost curves are heading. This cluster tracks those bets.

Serving infrastructure also intersects directly with security posture. Container deployments inherit every existing container vulnerability alongside a new class of AI-specific threats: model-weight theft, prompt injection through sidecar services, and supply-chain attacks targeting the Python dependencies that wrap inference engines. Infrastructure coverage at Groundy treats the operational and security dimensions as a single problem, not separate lanes.