Ask a question in plain English — results are ranked by meaning, with a keyword fallback.
10 results for “local LLM” · keyword match
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.
Almost every AI automation ROI claim is unfalsifiable. Twelve lessons on the four cost meters people miss, why time saved is the weakest number you have, computing payback instead of a percentage, and deciding the kill threshold before you need it.
Every model comparison you read was written against someone else's task. Twelve lessons on building the evaluation that produces your answer, pricing the choice properly, and making it reversible before the model changes underneath you.
Apple and Google both ship a language model built into the phone itself, callable through a platform SDK at zero marginal cost per call, fully offline, with data that never leaves the device. Here is what Apple's Foundation Models framework and Google's Gemini Nano actually do,
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
Z.ai's GLM-5.2 ships open weights under MIT and reportedly leads the open-weight field on SWE-bench Pro while trading blows with Opus 4.8 on FrontierSWE and MCP-Atlas, at roughly one-sixth of GPT-5.5's blended cost. The catch is a hosted-API data-residency caveat.
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.
Most "AI stack" posts hide the bill. Mine runs under $10/month. Here is every tool, the exact tier, the real cost, and the job each layer does for my one-person content business.
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.
