Run Local Large Language Models: Privacy & Performance

In today's AI-driven world, large language models (LLMs) are becoming increasingly prevalent. While cloud-based LLMs offer convenience, running them locally provides unparalleled privacy and control over your data. This guide explores the benefits of local LLMs and provides practical steps to get started.

What are Local LLMs?

Local LLMs are Large Language Models that run directly on your computer rather than in a remote cloud environment.

This approach provides users with complete control over their data and processing activities, offering a robust alternative to cloud-based services like OpenAI or Azure. By running LLMs locally, you ensure that your sensitive information remains on your device, safeguarding against potential privacy breaches and data exposure. These models allow for the implementation of chatbots, code generation tools, and various natural language processing (NLP) tasks without relying on an internet connection, making them ideal for use in airplane mode or other offline scenarios. It's also beneficial to repeatedly mention key terms like local LLM, large language models, or local machine to further assist with SEO efforts for this article, since the goal is to provide the reader with information they're actively searching for. The benefit of local deployment can lead to significantly reduced latency, as data doesn't need to travel to remote servers for processing, resulting in faster response times and enhanced user experience. This is particularly advantageous in applications where real-time interaction is crucial.

Hardware Considerations

To run large language models effectively on your local machine, certain hardware prerequisites are necessary.

A dedicated video card with sufficient video RAM (VRAM) is essential. The minimum recommended video card is typically an NVIDIA GeForce RTX 2080 or 3080, although some users have reported success with older cards like the GTX 1080. However, performance may be limited on less powerful GPUs. Aim for at least 10GB of VRAM to handle larger models smoothly. Using a robust video card ensures the efficient processing of complex computations, directly impacting the speed and responsiveness of the LLM. If you only have an intel graphics card you probably won't be able to run it. A computer's CPU and memory also play a crucial role. While the GPU handles the primary workload, a fast CPU and ample system RAM (16GB or more) ensure the overall system operates efficiently, preventing bottlenecks that can slow down performance. Optimizing your hardware setup can significantly improve the capabilities of the models you're using. Therefore, a balanced configuration is the key to achieving the best results when working with large language models locally.

Here's a quick hardware recommendation table:

Software and Installation

Setting up the software environment is a critical step in running local LLMs. Several tools and platforms facilitate the process, each offering unique features and capabilities. LM Studio and Ollama are two particularly popular options. LM Studio is a comprehensive tool that supports Windows, Mac, and Linux, providing a user-friendly interface to discover, download, and run local LLMs. Ollama, on the other HAND, focuses on simplicity, offering a streamlined experience for managing and deploying LLMs through command-line interfaces. After setting up the proper hardware, users need to install the appropriate software to leverage their GPU. It is worth repeating the importance of having a good video card if you plan to run LLMs. In practice, with models being so large it requires the extra computing power of a video card, you are not likely going to be able to use an LLM without one.

Installing LM Studio

1. Download LM Studio: Visit the LM Studio website

   (lmstudio.ai) and download the appropriate version for your operating system (Windows, Mac, or Linux).

2. Install the Application: Follow the installation instructions to set up LM Studio on your machine. The process is straightforward and typically involves running the downloaded installer.
3. Open LM Studio: Launch the application once the installation is complete. You'll be greeted with a user-friendly interface designed to simplify LLM management.

Selecting and Downloading a Model

1. Navigate to the Model Discovery Section: Within LM Studio, find the section dedicated to discovering and downloading models.
2. Browse Available Models: LM Studio offers a curated list of models. Here are a few good choices to load and pre-install from the chatbot section

   :

   • Code Llama: Ideal for coding-related tasks.
   • Mistral: A general-purpose model suitable for various applications.
3. Choose Your Model: Select a model that suits your specific needs. The models from Microsoft's Phi-2 are interesting, and they focus on toxicity reduction.
4. Download the Model: Click on the download button associated with the selected model. LM Studio will handle the download and installation process automatically.

Configuring and Running the Model

1. Open the Chatbot Interface: Access the chatbot interface in LM Studio, designed for interacting with the downloaded models.

2. Select a Model to Load: From the chat window, choose the downloaded model to load into the chat session. If you want a fast response, increase the amount of the loaded mode into GPU.

   

3. Configure Model Settings: Adjust any necessary settings, such as the GPU acceleration to optimize performance.

4. Start Chatting: Begin interacting with the model by typing your prompts in the chat window.

Creating a Local OpenAI-Compatible API Server

1. Navigate to the configuration option that makes a local server which behaves like an OpenAI API.

   

2. Enable the Local Inference Server: You now can start a server from this tool.

3. To ensure the tool is working copy over an endpoint and test from localhost.

Upfront Investment

One of the main differences between cloud based and local LLMs is the pricing structure. Local LLMs require investment in dedicated hardware, specifically a computer equipped with a capable video card and sufficient RAM. Initial costs can range from several hundreds to thousands of dollars, depending on the performance level desired. This upfront investment includes the cost of the GPU, system memory, and other components necessary to run the models efficiently.

Long-Term Savings

While the initial outlay for local LLMs may be significant, the long-term savings can be substantial. Cloud-based LLMs often operate on a subscription or usage-based model, where costs accumulate over time as you process more data. In contrast, with local LLMs, you only pay for the hardware once. After initial hardware purchases, you can repeatadely run these programs and LLMs in a cost effective manner.

Benefits of Running LLMs Locally

Here is a short summary on why you may want to deploy local large language models on your local machine:

• Enhanced Privacy: Complete control over your data with no need to transmit sensitive information to third-party servers.

  

• Reduced Latency: Faster response times due to local processing, eliminating the need for data to travel to remote servers.

• Offline Access: Ability to use the models even without an internet connection, providing uninterrupted access in airplane mode or other offline scenarios.

• Customization and Control: Greater flexibility to tailor the models to your specific needs and workflows without external dependencies.

Offline Chatbots

Develop chatbots that can function entirely offline, perfect for situations where internet connectivity is unreliable or unavailable.

Code Generation

Use local LLMs to generate code snippets and complete programming tasks without relying on external APIs, ensuring privacy and security of your projects.

Document Analysis

Analyze sensitive documents and extract key insights without transmitting the data to third-party services, ensuring confidentiality.

Personalized Learning

Create personalized learning experiences by tailoring the models to individual student needs, all while maintaining data privacy and control.