OpenAI-Compatible Local APIs: Swap the Base URL

Many local runtimes expose OpenAI-compatible APIs, which lets students reuse familiar SDK patterns while changing where inference runs. In local AI, the model family is only one part of the system. The runtime, file format, serving path, hardware budget, evaluation set, and safety policy decide whether the model becomes useful.

Current source signal

Build the small version

Write one tiny client that can target cloud, LM Studio, Ollama, or vLLM by changing a base URL and model name.

1. 1Define the user task in one sentence.
2. 2Choose the smallest model and runtime that might pass that task.
3. 3Run one happy-path prompt and one failure-path prompt.
4. 4Record speed, memory pressure, output quality, and the exact reason for any failure.
5. 5Write the operating rule you would give a non-expert user.

client_targets:
  cloud:
    base_url: https://api.vendor.example/v1
  lm_studio:
    base_url: http://localhost:1234/v1
  vllm:
    base_url: http://server.local:8000/v1

constant:
  message_format: OpenAI-style chat messages
variable:
  model behavior and limits

The big idea: swap the base URL. A local model app is not done when the model answers once; it is done when the whole workflow can be installed, measured, trusted, and recovered.