How to Create a Local Claude Code with Unlimited Tokens: A Step-by-Step Guide

These days, most people can’t stop raving about the genius of Claude AI. It’s no surprise, since the bot impresses users with its capabilities almost every day. Many believe it has surpassed ChatGPT. Now users can be divided into AI clans, as conversations about bots often turn into heated debates over which one is better.

Ultimately, we won’t be discussing which service to choose or which bot has the most skills. Since they have all long since reached a high level of development and offer us a wide array of “services.” There’s only one thing to say here, which has evolved from ordinary advice into a law of the digital world—use AI.

For a media buyer, artificial intelligence has become an integral part of the job. AI is everywhere now. If you don’t work with it and don’t incorporate this into your workflow, you simply risk becoming uncompetitive in the traffic market.

We’re not urging you to hand over your entire job to AI. That’s impossible anyway. Especially when it comes to a media buyer’s work. Neural networks have internal censorship that prevents them from creating absolutely everything. So don’t put your own hands and head on the shelf—we’ll still need them.

AI should be used to better automate processes and simplify work overall. Yes, it’s often an expensive endeavor with plenty of nuances. However, these are the realities, and there’s only one choice here: either accept the rules of the game and scale up, or ignore innovation and fall behind.

In this article, we’re talking about Claude AI. We recently wrote an article about this AI assistant and how it can help with landing page optimization. Today, check out this guide on how to set up a local Claude Code instance with unlimited tokens.

Guide: How to Set Up a Local Claude Code Instance with Unlimited Tokens

First, you need to understand how LLM works—the foundation of the process on which everything depends.

Let’s look at how LLM works using Claude Code as an example, which is the focus of this article. Claude Code is a CLI tool that sends an API request to Anthropic’s servers for every query. Once on the servers, all information is processed by the LLM. Currently, Claude uses one of the best models—Opus 4.6.

As a result, we receive a response to the request: a text message, a landing page, code, and so on.

If we’re creating a local Claude Code, we need a local LLM to which we’ll send requests via the CLI tool.

Let’s talk about the pros and cons right away

Pros:

The local model is uncensored. So, with it, you can generate much more than with standard models.
An additional advantage is the ability to train the model on your own data. If your work involves specific aspects that require a customized approach, you can configure the model to handle them.
Since the model is local, the data you upload to it won’t go anywhere. That is, it will only be on the device where the model is installed.

Cons:

In this case, we are downloading an open-source LLM, which differs from Opus 4.6. This means it cannot be used to build SaaS platforms or work on large-scale projects. Yes, you can try to train the neural network to perform more complex tasks. However, it is still better to give it simpler tasks.
A powerful device is required. In any case, the model consumes a lot of resources, and a weak computer may simply not be able to handle it.

Working with Ollama

Ollama is a tool that helps you run local LLMs without relying on Anthropic. It runs directly on your computer, not on a third-party server or in the public cloud.

Ollama allows you to run popular open-source models (Mistral, LLaMA, DeepSeek) and interact with them in the same way as with the Claude API.

Step 1: Download Ollama

Download Ollama (select the file for your operating system)
Unzip the file and complete the installation
Next, open the command line (CMD) in Ollama and enter the command: ollama –version

Step 2: Selecting an LLM

At this stage, pay special attention to your computer’s processing power, as a lot depends on it here. Local models consume more resources on their own.

For a powerful device: qwen2.5-coder:14b or queen3-coder.
For a less powerful device: qwen2.5-coder:7b.

Once you’ve selected the model you need, enter the command: ollama pull qwen2.5-coder:14b (replace this model with one that’s suitable for your specific computer).

After entering the command, wait until the installation is fully complete. This may take some time and use up disk space. The LLM is quite large.

Once the installation is complete, enter the command: ollama run qwen2.5-coder:14b (your model) and check if the neural network is working properly.

Step 3: Connecting the LLM to Claude Code

By combining Ollama and Claude Code, you get a full-fledged assistant that not only chats but also works with files: reads them, edits them, makes changes, and more. Without Claude, Ollama won’t give you the results you want.

First, make sure you’ve already installed Claude Code on your computer. If not, you know what to do.
Instead of the Anthropic API, we’ll connect Claude Code to a local Ollama server. Enter the command: `ollama serve`. This will start Ollama. You can find this guide at: http://localhost:11434/
Then enter the command: ollama launch claude –modelqwen2.5-coder:14b. Claude will then connect to the local model you installed.

In summary

A local LLM is particularly well-suited for large teams when work requires accounting for many specific details. Overall, it’s a convenient tool that simplifies work, opens up more opportunities for you, and automates processes.

Now you can experiment with AI however you like. The key is to finally integrate it into your workflow and master it properly. As we’ve mentioned, understanding AI and knowing how to work with it makes you competitive in the market. This opens up more opportunities for you—ones you might not have even known about when working solo.