I connected a local model to Obsidian with MCP and it’s better than NotebookLM and ChatGPT combined

Local LLMs Powered by MCP – The 2025 Offline AI Revolution { "@context":"https://schema.org", "@type":"Article", "headline":"Local LLMs Powered by MCP – The 2025 Offline AI Revolution", "author":{"@type":"Person","name":"[Your Name]"}, "datePublished":"2025-11-27", "publisher":{"@type":"Organization","name":"TechInsight Enterprise"}, "mainEntityOfPage":"https://www.techinsightenterprise.com/local-llms-powered-by-mcp" } Local LLMs Powered by MCP – The 2025 Offline AI Revolution In the last year, the Model Context Protocol (MCP) has moved from a niche research prototype to an industry‑standard for on‑premise large language model deployment. Enterprises that once relied on cloud‑based GPT‑4o or Claude 3.5 are now running Llama 3‑70B and NVLM‑D‑72B directly on their own GPU clusters, achieving latency below 200 ms, zero outbound traffic, and a cost per inference that is less than one tenth of the public API model. 1. MCP: The Missing Piece in Local LLM Adoption MCP solves three core pain points: Contextual data routing : It maps structured knowledge bases to prompt tokens without exposing raw documents to the model. Dynamic inference scaling : It negotiates GPU memory allocation on‑the‑fly, allowing a single node to serve up to 4 concurrent user sessions with consistent throughput. Model version isolation : It keeps each deployment at a specific LLM checkpoint (e.g., Llama 3‑70B v1.2 or NVLM‑D‑72B v0.9) while enabling seamless upgrades via mcp-update . 2. Technical Deep Dive: Running Llama 3‑70B and NVLM‑D‑72B with MCP The following architecture diagram (simplified for clarity) shows the data flow from a corporate knowledge base to a local inference node: Knowledge Base ──► MCP Context Engine ──► Prompt Generator │ │ ▼ ▼ Retrieval Service ──► Embedding Index ──► LLM Inference (GPU) Key implementation steps: Indexing : Use mcp-index to create a dense embedding index from the internal document store. The command supports hybrid retrieval (vector + keyword) and outputs a l