In this article
LM Studio presets store chat and inference choices; per-model defaults separately control the settings applied on model loading.
LM Studio presets save system prompts and inference parameters, while per-model defaults control loading. A preset that restores writing instructions should not be expected to restore every model load setting; the current preset format excludes load parameters.
The concrete application boundary
The preset documentation distinguishes reusable chat configuration from load configuration. A named preset can carry a system instruction and inference choices such as sampling or output limits. Per-model defaults are edited from the model’s gear control in My Models and are applied the next time that model loads, including loading through lms. A model already in memory is therefore a different state from its next-load defaults. This explains why changing a saved default and continuing an existing conversation may not demonstrate the intended load behavior. Keep the model identity, active chat preset, and saved model load defaults in separate fields rather than treating one preset name as the complete environment (LM Studio Config Presets).
A practical route
For a reusable writing style, save the system prompt and intended inference settings as a named preset through the chat preset manager. For load behavior, open My Models, click the specific model’s gear icon, and edit its default parameters. Save there, then inspect the next load of that same model. When experimenting with a load-time override, distinguish the temporary choice from the option to save it as the model’s default. Do not overwrite unrelated defaults merely to repair an instruction preset (LM Studio Per-model Defaults).
A worked example
A user saves a concise-answer preset and expects it to reproduce a previous load configuration on another chat. The writing instruction returns, but the model loads with its own defaults. The correction is to preserve the concise preset for its actual purpose and save the relevant model load choice under My Models. Reopen a fresh chat with the preset, then separately inspect a subsequent load of the intended model.
| Check | Record |
|---|---|
| Chat configuration | The concise system instruction and inference settings saved as a preset. |
| Load configuration | The chosen model’s defaults saved through its My Models gear control. |
| Observed state | The currently loaded model and the settings used at its next load. |
Reading the failure correctly
Importing a preset is not importing model weights or proving a load change happened. A temporary load override can also differ from the saved default. Older preset tutorials may describe another format, so use the current format documentation rather than inferring behavior from a legacy file name or an old screenshot (LM Studio chat management).
What completes the check
The reliable distinction is reusable conversation behavior versus model loading behavior. Saving each in its documented location makes a local workflow repeatable without promising that a shared preset recreates another machine’s entire runtime state.
Related background: llama.cpp server: a local API from a GGUF model, VRAM for LLMs: why model size is only part of the calculation.