Local LLM (Private LLM)

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What is a Local LLM?

A local large language model (LLLM) is a large language model (LLM) that is deployed on an organization’s own hardware or private cloud. Local LLMs may also be referred to as private LLMs.

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What is a Local LLM (Private LLM)

Key Takeaways

  • Local LLMs run on an organization’s own hardware or in a private cloud.
  • LLLMs allow businesses to use proprietary and/or regulated data for model development and fine-tuning.
  • Businesses that use LLLMs also maintain control over data that’s acquired through user interactions.
  • Running an enterprise-level LLLM requires significant computational resources and machine learning expertise.
  • While it’s technically possible to run a smaller LLM locally on a desktop computer, the user experience (UX) is likely to be frustrating.

How Local LLMs Work

Local large language models work just like ChatGPT, Gemini, or any other third-party large language model. Basically, the end user enters a prompt, and the LLLM responds.

The difference is that local LLMs can be fine-tuned with the organization’s proprietary data to enhance performance for specific tasks. This allows LLLMs to provide useful responses for tasks that require domain knowledge.

Why Use Local LLMs?

When companies deploy their own LLLM, they can use customer data to fine-tune the model for specific business needs. This is not possible with third-party LLMs like ChatGPT or Gemini because OpenAI and Google control model development, and in many countries, customer data is regulated.

If an employee was to use Gemini to analyze customer data and generate a report, for example, they could end up violating GDPR data privacy rules or data sovereignty regulations. If they were to use a local LLM, however, logs would provide documentation that personally identifiable information (PII) always remained private and under the company’s control.

Use Cases for Local LLMs

As generative AI becomes integrated into software applications people use for work and entertainment, it’s expected that organizations and individuals will experiment with local LLMs.

Use cases include:

  • Sophisticated chatbots that can reference a company’s internal resources to answer domain-specific questions or make recommendations.
  • Trading bots that can review large amounts of proprietary data, analyze trends, and make data-driven decisions autonomously.
  • Writing assistants that can generate domain-specific marketing content, technical documentation, training resources, or legal documents.
  • Gaming bots that can monitor and analyze player interactions and make recommendations.
  • Personal desktop bots that can be used as a sandbox game.

The Role of Open Source in Local LLMs

Open source models are free to use and can be customized, but they also tend to require the most technical knowledge.

Having said that, their benefits in terms of accessibility, transparency, and community collaboration can outweigh the initial technical challenges.

Local LLM Privacy Concerns

Businesses that decide to run a large language model in their data center or in a private cloud still need to ensure their deployment complies with data protection regulations.

Anonymizing sensitive data before using it to fine-tune a foundation model can help protect privacy, but it’s not a foolproof differential privacy strategy.

If you are going to use sensitive data with a local LLM, here are some best practices you may want to consider:

  • Aggregate and process the minimal amount of data required to train the LLM for its intended purpose.
  • Implement strict access controls to ensure that only authorized personnel can access internal prompts and model outputs.
  • Encrypt data at rest and in transit to protect it from unauthorized access.
  • Conduct regular data management audits and model performance reviews to proactively identify and address privacy risks.

Local LLM Examples

Hugging Face is one of the best places to find LLMs you can run locally. Each model on Hugging Face typically comes with comprehensive documentation, including information on the model’s architecture, training data, performance metrics, and potential use cases.

Popular LLMs to run locally include versions of LLaMA, GPT-Neo, and DistilBERT.

How to Run LLMs Locally on Your Computer

Due to resource constraints running a full-scale LLM on a desktop computer is not practical for many people. You may be able to run a small foundation model locally, however, if you have the right hardware, some technical expertise, and a lot of patience.

Here’s what you’ll need to do:

  1. Research local LLMs versions on Hugging Face. The best local LLM choice will depend heavily on your desktop’s processor and RAM.
  2. Set up the necessary support software. Depending on what model you choose, this may involve installing Python, downloading TensorFlow, and manually mapping dependencies.
  3. Download and install the pre-trained model.
  4. If given the option, provide the model with data you want it to use in responses. This will allow you to fine-tune the foundation model.
  5. Input prompts and use the model. This is how you will train your local LLM to use the additional data you provided.
  6. Monitor resource use to make sure your hardware is not being overwhelmed, and the model runs smoothly.

Now that you know how to run a local LLM on your desktop, go ahead and give it a try! You can experiment with the LLM offline – and if you’re lucky, your interactions with the model will inspire new use cases.

Local LLMs Pros and Cons

Like any new deployment model, local LLMs have their advantages and disadvantages.

Pros
  • Control over data and model development
  • Ability to use proprietary data and stay compliant
  • Potential long-term cost savings for high-volume use
Cons
  • Upfront costs for in-house deployments can be high
  • Requires skilled personnel with expertise in machine learning (ML), model deployment, and infrastructure management
  • Continual maintenance will be required to avoid model drift

The Bottom Line

Most local LLM definitions focus on business use. Some definitions focus on hobbyists who want to experiment with running a large language model on their desktop computer offline.

As deep learning algorithms become easier to use and mobile devices become more powerful, however, don’t be surprised when vendors begin to brag about how their smartphones and tablets can run LLMs locally. When this happens, you can expect to see smarter digital assistants that don’t require internet connectivity.

FAQs

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References

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Margaret Rouse
Technology expert
Margaret Rouse
Technology expert

Margaret is an award-winning writer and educator known for her ability to explain complex technical topics to a non-technical business audience. Over the past twenty years, her IT definitions have been published by Que in an encyclopedia of technology terms and cited in articles in the New York Times, Time Magazine, USA Today, ZDNet, PC Magazine, and Discovery Magazine. She joined Techopedia in 2011. Margaret’s idea of ​​a fun day is to help IT and business professionals to learn to speak each other’s highly specialized languages.