When OpenRouter Is Unavailable, How Do You Switch Your App to a Local LLM?

LM Studio's API¹ server receives code and questions from the app, runs them through the local model, and sends the answer back. First check whether your app's settings offer OpenAI-compatible or Custom provider. This is where you will enter LM Studio's server address (Base URL) and model ID. LM Studio server guide · Compatible API and Base URL

The function below calculates what percentage part represents of total. Send it to the local model and ask what happens when total is zero and how to fix it.

Install LM Studio and find a model in the Discover tab. Check its model card for a format supported by an LM Studio runtime², such as GGUF³ or MLX⁴, and choose a quantized file that fits your memory. If your task needs tool calls⁵, check that the selected model supports them. Model download guide

After downloading, select the model in the Chat tab and load it into memory. Complete any runtime installation prompt⁶ on the first run. You can import an external GGUF into LM Studio's model folder with lms import --copy. If only OpenRouter is unavailable but your internet connection works, you can still prepare the model. To prepare for losing internet access too, download the model and runtime in advance. Model search, downloads, runtime installation, and updates require a connection. Offline operation · Import models

Open the Developer tab in LM Studio and turn on Start server. The default example port is 1234, and Server Settings can change it, so use the actual API address shown in the app. Leave Serve on Local Network off and keep the server on this computer's localhost. Start the server · Server settings

Open http://localhost:1234/v1/models in a browser or terminal and copy an id from its data array into the app. If you changed the port, replace 1234 with the actual number. With JIT loading enabled, the list may include models stored on disk but not yet loaded into memory. Load the model first from the Chat tab's model loader, or enable JIT in Server Settings. List models API · Server settings

With the default authentication setting, this model-list request needs no external API key. If you enabled Require Authentication in LM Studio, use a local API token⁷ created through Manage Tokens in Server Settings, and enter it in the app only if its authentication field requires one. Never put an OpenRouter or other cloud key in a localhost request. LM Studio authentication

Create a local profile by choosing OpenAI-compatible or Custom provider in your app's model-provider settings. Do not duplicate the OpenRouter profile because its provider type or automatic routing may remain. Set Base URL to http://localhost:1234/v1 and use the ID copied from /v1/models. If the app offers automatic provider fallback or a model router, turn it off so failed requests are not sent to an external service. LM Studio Base URL format

After saving the profile, select the local profile and open a new chat. Review the messages and attachments the app includes, then enter the code and question you want reviewed. Bring over any code or conditions from the previous cloud conversation that are needed for this review.

Ask What is 1 + 1? in the app. If an answer returns, the basic request-and-response connection works. If no answer arrives, use the error table in the next section. To also check that the request actually reached LM Studio, use the optional log check below.

To inspect request logs, run lms --help in a terminal and check that the command list appears. If the command is not found, follow the LM Studio CLI setup guide. Then run the log command below and leave the terminal open.

lms log stream --source server

If you started the log stream, repeat the same short question and check that the request appears. Log command guide Once the connection is checked, send the example function with Find the bug and suggest a minimal fix. What happens when total is 0? After the model identifies ZeroDivisionError when total == 0 and proposes a fix, compare how the code handles zero and what it returns.

A local model may have a different context limit or tool-calling support from the cloud model. For a long request, keep the code and question but shorten the input; for a task that needs tools, compare results with the app's tool integration off and on. For this example, check that the model finds the division-by-zero issue and that its proposed handling produces the intended return value.

When an error occurs, note the first status code or message in the app and LM Studio logs. Find the symptom in the table, change one item at a time, and repeat the same short question. Check whether a request appears in the server log to help distinguish an app connection problem from a model response problem. If task results differ after a valid response, also check context length⁸, tool support, and output limits. Server status guide

Note the server port, model ID, and runtime so you can continue the same check in a new chat later. For preserving model files with runtime settings, see the local LLM backup and restore guide; for the scope of related policy, see the AI export controls and local models guide.