This topic examines the practical tradeoffs between deploying large language model inference at the edge, on dedicated accelerator appliances, and on-premises rack servers — a landscape changing fast as vendors push custom inference chips and optimized stacks. Demand for low-latency, privacy-preserving inference for applications such as local RAG, offline assistants, and embedded controls has driven a mix of device-level solutions, stand-alone accelerators, and full on‑prem platforms. Key considerations are latency, throughput, power efficiency, model compatibility, and operational complexity. Edge deployments prioritize minimal latency and user privacy but are constrained by memory and thermal limits; accelerator appliances (including custom inference chips) trade higher throughput and model size for increased power and integration needs; on-prem rack servers offer scale and centralized management but demand more infrastructure and ops expertise. Recent market trends emphasize quantized models, MCP-compatible servers, and tighter hardware–software co‑design to ease deployment. The tool ecosystem reflects these needs: FoundationModels provides macOS-based text generation for local MCP clients, Local RAG and Minima enable privacy-first, on‑device document search and RAG workflows, Multi-Model Advisor orchestrates multiple local models for diverse perspectives, and Producer Pal demonstrates domain-specific, embedded LLM control in music production. Together these illustrate how MCP servers and local RAG components map to each deployment tier: lightweight on-device inference, containerized on‑prem RAG, and accelerator-backed servers for larger models. Evaluations should focus on model compatibility (quantization/INT8, sparsity), MCP support, latency targets, power envelope, and operational constraints. Understanding these axes helps teams choose between edge, accelerator, or on‑prem inference paths as custom silicon and privacy requirements continue to reshape real‑world LLM deployments.