Tag
Q4_K_M, GPTQ-4bit, AWQ: the names encode 2 separate choices, and the format matters more than the bit count. Measured quality by level on Llama-3.1-8B, the memory math including the KV cache, and which formats your hardware can actually run.
Most small businesses decide what data may reach their AI tools retroactively, when a client asks. Eleven lessons on the inventory, the three data tiers, what zero retention actually covers, and why the model is rarely the leak.
Local is free is the sentence that starts most bad local-LLM projects. Eleven lessons on naming a real reason, the costs that never appear on an invoice, sizing hardware to the model, proving privacy properly, and knowing when to give it back.
An official US evaluation says DeepSeek V4 Pro lags the frontier by about eight months. It gets quoted as a verdict. My take: for most real coding and automation work, an open-weight model that is eight months behind at a fraction of the price is not a compromise, it is the
A local LLM is not free; it trades a per-token API bill for a mostly fixed monthly cost. This reproducible break-even model loads both columns (hardware, electricity, and token volume) and shows exactly where local stops costing more than the API.
The credential test fails because n8n looks inside its container, not at Ollama on your host. This guide shows the exact Base URL for every setup: native, Docker Desktop, and Linux server.
Running a local LLM cuts your AI API bill to $0 and keeps client data off third-party servers. This breakdown of Ollama, LM Studio, and Jan tells you exactly which tool fits a solo operator's workflow.
