Best Local LLMs for Android Development (Offline Guide)

8/10/2025

Hey there! So you're looking for the best local LLM for Android development & you're a fan of Claude Code. That's a great question, & honestly, it's a topic that's evolving at lightning speed. Here's the thing, "Claude Code" itself isn't a local LLM you can just download & run. It's an AI coding assistant that, by default, uses Anthropic's powerful models in the cloud. But, the good news is you can ABSOLUTELY get a similar, if not better, experience using a local LLM that runs entirely on your own machine. This gives you privacy, offline access, & more control.

Let's break down how you can achieve this for your Android development workflow.

The Two Main Paths to Local LLM Bliss for Android Devs

You've got two main options when it comes to running a local LLM for Android development. The best one for you will really depend on your specific needs & what you're trying to accomplish.

Path 1: The Powerhouse - Running a Local LLM on Your Development Machine

This is, by far, the most practical & powerful approach for day-to-day coding. You'll run the LLM on your Mac, Windows, or Linux machine, the same one you use for Android Studio. The performance is way better than what you'd get on a phone, & you can use much larger, more capable models.

Step 1: Choose Your Local LLM for Coding

There are some fantastic open-source LLMs out there that are specifically trained for code generation. Here are a few of the top contenders right now:

Code Llama: Developed by Meta, this is a very popular & capable model for coding. It comes in different sizes (7B, 13B, 34B parameters), so you can pick one that works well with your hardware.
Mistral: Mistral's models, especially the 7B one, are known for being incredibly performant for their size. They're a great choice if you have a slightly older machine but still want good results.
Qwen 2.5 Coder: This is another strong contender, with some benchmarks showing it to be very competitive with the top models for coding tasks.
DeepSeek-Coder: This family of models is also highly regarded in the developer community for its coding prowess.

Pro-Tip: A larger parameter model (like a 13B or 34B) will generally give you better results but will also require more RAM & a more powerful GPU. A 7B model is a good starting point for most modern laptops.

Step 2: Get the Tools to Run Your Local LLM

You don't have to be a machine learning expert to get these models running. There are some super user-friendly tools that make it a breeze:

Ollama: This is a fantastic command-line tool that lets you download & run popular LLMs with a single command. It's my personal favorite for its simplicity.
LM Studio: If you prefer a graphical interface, LM Studio is an excellent choice. It makes it easy to discover, download, & chat with different local LLMs. You can also use it to set up a local server, which is key for the next step.

Step 3: Integrate Your Local LLM with Android Studio

This is where the magic happens! You can get a coding assistant right inside Android Studio that's powered by your local LLM. Here's how:

Install the "Continue" Plugin: Go to the plugins marketplace in Android Studio & search for "Continue". Install it.
Configure "Continue" to Use Your Local LLM:
- Start your local LLM server using Ollama or LM Studio. For example, in LM Studio, you'd load your chosen model & then click "Start Server".
- In Android Studio, open the "Continue" sidebar. It will likely ask you to configure your model. You can select "LM Studio" or "Ollama" from the list of providers.
- It should autodetect your running server, & you'll be good to go!

Now, you'll have a coding assistant in Android Studio that can help you write code, explain things, & even debug, all while using your private, local LLM.

What About a "Claude Code" Experience in the Terminal?

If you love the terminal-based workflow of Claude Code, you can replicate that too! It's a bit more advanced, but totally doable. The basic idea is to use a proxy that translates the API requests from a tool like Claude Code to a format that your local LLM server (from Ollama or LM Studio) can understand. This lets you use the Claude Code interface with your own local model, which is pretty cool.

Path 2: The Experimental Frontier - Running an LLM Directly on Your Android Device

This approach is more cutting-edge & has more limitations, but it's also really exciting. Here, the LLM runs directly on your Android phone. This could be useful for building apps that have on-device AI capabilities, or for some very light coding tasks on the go.

Frameworks for On-Device LLMs on Android

Google's AI Edge Gallery & MediaPipe: Google has been making a big push into on-device AI. Their AI Edge Gallery app is a great way to see what's possible. They use the MediaPipe framework to run models like Gemma (a lightweight version of their Gemini models) directly on your phone. You can integrate this into your own apps to have on-device generative AI.
picoLLM: This is another great option for running LLMs on resource-constrained devices like phones. They offer hyper-compressed models that are optimized for on-device use.
MLC LLM: This is a project that allows you to run various LLMs locally on your Android device through an app called MLC Chat.

The Reality of On-Device LLMs for Development

Honestly, for your main development work, you'll want to stick with Path 1. On-device LLMs are still limited by the processing power & RAM of your phone. The models you can run are much smaller, & the performance won't be as good.

However, if you're building an Android app that needs some AI features, using an on-device LLM can be a great way to do it. For example, you could build an app with a smart chatbot for customer support. And for something like that, a platform like Arsturn could be super helpful. Arsturn helps businesses create custom AI chatbots trained on their own data. So, you could use Arsturn to build the chatbot's brain & then use an on-device LLM to run it in your app for instant, private customer engagement. Pretty cool, right?

So, What's the "Best" Local LLM?

Here's the bottom line:

For your day-to-day Android development in Android Studio, the best setup is a local LLM like Code Llama or a good Mistral model, running on your computer with Ollama or LM Studio, & integrated into Android Studio with the "Continue" plugin. This gives you the power you need & the privacy you want.
For experimenting with on-device AI in your Android apps, check out Google's AI Edge Gallery with Gemma models. It's a great way to see the future of mobile AI.

The world of local LLMs is moving incredibly fast, so what's best today might be different in a few months. But for now, this should give you a solid starting point.

Hope this was helpful! Let me know what you think or if you have any other questions. Happy coding