Unable to provide answers specific to your company’s business

The learning data for LLMs (Large Scale Language Models) is mainly created from public documents on the Internet. For this reason, while general-purpose LLMs have general knowledge, they lack specialized knowledge specific to specific companies or domains, making it difficult to understand vocabulary and context specific to industries or individual companies. To solve this, it is necessary to build custom models or use a mechanism called RAG (Retrieval-Augmented Generation) to supplement the knowledge that the base model does not have.

I don't want to put confidential information on the cloud.

In a cloud environment, data is stored on an external server, so there is a risk of confidential information such as in-house know-how and customer information being leaked. In addition, although the security measures provided by cloud service providers (CSPs) are generally high, the ultimate responsibility for data management lies with the company, so caution is required, especially for highly confidential information. For these reasons, many companies tend to avoid uploading confidential information to the cloud and choose to process it in an on-premise environment.

If you would like to learn more about local LLMs, please see this article.

• Introduction to Local LLMs: Part 1: A complete guide to the basics and application of Local LLMs
• Introduction to Local LLMs - Part 2: How to actually build a local LLM?
• Introduction to Local LLM ~Episode 3: Customization and Fine Tuning~

Benefits of a Local LLM

1. Security and Privacy

Local LLM processes data in-house, eliminating the need to transmit confidential or personal information externally and reducing the risk of data leakage.

2. Customizability

You can freely customize the model to fit your company's specific business needs, allowing it to learn industry-specific knowledge and terminology.

3. Performance

The lack of network latency enables real-time inference and the ability to process large amounts of data quickly.

4. Cost Management

Although the initial investment may be high, in the long run it is easier to estimate costs and allows for more stable operation than pay-as-you-go cloud services.

As such, Local LLMs are a very useful option, especially for companies that place a premium on security and customizability.

Building a GPU environment (infrastructure)

To operate local LLM effectively, a high-performance GPU environment is required. There are several barriers to building such a system.

Choosing the best hardware

In order to select the optimal GPU resources for what you want to achieve, you need knowledge of generative AI models and an environment to actually try them out in. Also, since GPU infrastructure is expensive, if you cannot select the optimal hardware, it may end up being a waste of investment.

Infrastructure environment construction and maintenance

Building an infrastructure environment requires specialized knowledge of servers, networks, and the software implemented on them. Even after the GPU environment is built, personnel who can handle hardware maintenance, upgrades, and troubleshooting are required. This can lead to increased operational costs and human resources.

Scalability Constraints

Compared to cloud environments, it can be difficult to expand resources in on-premise GPU environments. The inability to flexibly scale up and down in response to fluctuations in demand can be a barrier to adoption.

Building a local LLM (software)

There are also several challenges in building software to utilize local LLMs.

Technical Expertise

Building a local LLM requires a deep understanding of machine learning and natural language processing. In addition, it is necessary to make full use of advanced technologies such as model fine-tuning, Retrieval-Augmented Generation (RAG), and AI agents, so it is important to secure personnel with specialized skills.

Development Time

Customizations and integrations to meet your needs can require lengthy development times, making rapid deployment difficult, especially if they require an iterative trial and error process.

Choosing the right tools

There are many tools and libraries required to implement a local LLM, so it is important to choose the tool that best suits your company's purposes.

As described above, the introduction of local LLM involves cost and technical challenges in both infrastructure and software construction, and companies need to carefully consider the matter.