Why Run a Large Language Model Locally?

There are a few reasons why someone might want to run a large language model (LLM) locally.

• Privacy: If you are working with sensitive data, you may want to avoid storing it in the cloud. Running an LLM locally gives you more control over your data.
• Cost: Cloud-based LLMs can be expensive, especially if you are using them frequently. Running an LLM locally can save you money.
• Performance: If you need to use an LLM for a specific task, such as generating text or translating languages, you may get better performance by running it locally. This is because cloud-based LLMs are often shared by multiple users, which can slow them down.
• Customization: If you want to customize the LLM or train it on your own data, you will need to run it locally.

However, there are also some challenges to running an LLM locally.

• Hardware requirements: LLMs require a lot of computing power. You will need a powerful computer with a lot of RAM and storage space.
• Technical expertise: Running an LLM locally requires some technical expertise. You will need to know how to install and configure the software and how to use it.
• Maintenance: LLMs require regular maintenance. You will need to keep the software up to date and fix any problems that occur.

Here are some large language models that are available to run locally:

• LaMDA: LaMDA is a factual language model from Google AI, trained on a massive dataset of text and code. It can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. LaMDA is available to run locally using the Google Colab platform. pub.towardsai.net LaMDA large language model
• Bard: Bard is a large language model from Google AI, trained on a massive dataset of text and code. It can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Bard is available to run locally using the Hugging Face Transformers library. www.engadget.com Bard large language model
• Turing NLG: Turing NLG is a large language model from Hugging Face, trained on a massive dataset of text and code. It can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Turing NLG is available to run locally using the Hugging Face Transformers library. www.microsoft.com Turing NLG large language model
• GPT-Neo: GPT-Neo is a large language model from EleutherAI, trained on a massive dataset of text and code. It can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. GPT-Neo is available to run locally using the EleutherAI GPT-Neo repository. twitter.com GPT-Neo large language model
• Koala: Koala is a large language model from EleutherAI, trained on a massive dataset of text and code. It can generate text, translate languages, write different kinds of creative content, and answer your questions in an informative way. Koala is available to run locally using the EleutherAI Koala repository. bair.berkeley.edu Koala large language model

The best model for you will depend on your specific needs and requirements.

When choosing a large language model to run locally, you should consider the following factors:

• The size of the model: Larger models require more computing power and storage space.
• The type of tasks you want to use the model for: Some models are better suited for certain tasks than others.
• Your technical expertise: Running a large language model locally requires some technical expertise.

Final Thoughts

Overall, whether or not to run an LLM locally depends on your specific needs and requirements. If you are concerned about privacy, cost, or performance, or if you need to customize the LLM or train it on your own data, then running it locally may be a good option for you. However, if you do not have the necessary hardware or technical expertise, then you may want to consider using a cloud-based LLM.

You may review a naive attempt blog post about running an LLM locally here:

Attempt to Run A Large Language Model Locally