As I like to experiment a lot with AI, I always have to be careful and keep my token usage under control. And although the token cost has decreased over time for most models, the expenses can go up quite fast.
That is one of the reasons I like to use (Large) Language Models locally. There are multiple ways to run a model locally but my preferred way so far was Ollama (together with OpenWebUI). I also experimented with Podman AI Lab but I always returned to Ollama in the end.
Recently a colleague introduced me to LM Studio, another tool to run and test LLM’s locally. With LM Studio, you can:
- Run LLMs offline on your local machine
- Download and run models from Hugging Face
- Integrate your own application with a local model using the LM Studio SDK or through the OpenAI endpoints
- Use the built-in RAG support to chat with your local documents
More than enough reasons to give it a try…
Getting started
- I downloaded the installer from the website and followed the installation wizard. Once the installation has completed click on Finish:
- If you selected the Run LM Studio checkbox, LM Studio will be started immediately and you will be welcomed by an onboarding wizard:
- Click on the Get your first LLM button. The next onboarding step appears where it suggests me to download a first model(in this case DeepSeek R1).
- Notice the Enable local LLM service on login option. By checking this option, you can use the LM Studio local LLM server without having the LM Studio application open.
- Click on Download to download the suggested model.
- Once the download has completed, a Start New Chat button appears. Click on it to continue.
- We finally get the LM Studio UI in front of us. Let us first load the downloaded model by clicking on the Load Model button in the notification popup.
- Once the model is loaded, we can start asking questions by typing a message in the chat window and hitting Send.
Exploring some features
Now that we have the basics runnng, let’s explore some of the nice features that LM Studio has to offer.
If you click on the settings icon in the top right corner, you get a lot of options to easily tweak the used model.
For example you can easily enable structured output(if the model supports it):
Another feature I like is that you can switch between ‘User’ and ‘Assistant’ mode in the chat. This is useful for few-shot prompting and other scenario’s:
I already mentioned the RAG integration:
Don’t forget to check out the Developer tab as well where you can run the LLM server and expose OpenAI compatible endpoints to your applications:
It’s too soon to tell if I will use this instead of Ollama but it is certainly worth to give it a try.
More information
Run LLMs locally using Podman AI Lab
Running large language models locally using Ollama