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2024 will be the first year of “Local LLM”!

The emergence of ChatGPT has sparked a generative AI boom around the world.
However, because many generative AIs, including ChatGPT, run on the cloud, security concerns and cost burdens have been issues when companies use them.

In this context, "local LLM" is attracting attention.
Local LLM is a generative AI that companies can install and use on their own servers and PCs, and is expected to be a groundbreaking technology that will solve security and cost issues.

According to a survey by Andreessen Horowitz, 92% of Fortune 500 companies in the United States are already using ChatGPT, and the adoption of generative AI is progressing rapidly.
In addition, although the adoption rate of generative AI in Japanese companies remains at 10%, a 2.5-fold increase in budgets is expected, and adoption is expected to accelerate in the future.

In particular, local LLMs, which offer advantages in terms of security and cost, will represent a great opportunity for Japanese companies.

In this article, we will thoroughly explain everything from basic knowledge about local LLMs to the latest trends, implementation cases, and corporate implementation support services provided by Digirise.

table of contents

1. What is generative AI?
2. What is a Local LLM?
3. Why a local LLM is needed
4. Advantages and disadvantages of introducing local LLM
5. Local LLM Case Studies
6. Digirise's Corporate Implementation Support Program
7. Summary: Accelerate AI utilization in Japanese companies with a local LLM!

1. What is Generative AI?

Generative AI is AI that can generate different types of data, including text, code, images, audio, and video.
Traditional AI has been used primarily for data analysis and prediction, but generative AI has the ability to create new content.

For example, ChatGPT is attracting attention as a chatbot that can generate natural, human-like sentences, and can be said to be a representative example of text generation AI.
Other well-known image generation AIs include Stable Diffusion and Midjourney.

2. What is a Local LLM?

LLM (Large Language Model) is an AI model trained on huge amounts of text data that can generate natural, human-like sentences, answer questions, and translate.

Local LLM refers to the LLM being installed and used on a company's own server or PC.

3. Why is a local LLM desirable?

Local LLMs are gaining attention for the following reasons:

• Enhanced security: Cloud-based generative AI requires data, including confidential and personal information, to be sent to an external server, raising the risk of information leakage. With local LLM, data can be processed in the company's own environment, significantly reducing security risks.
• Cost reductionCloud-based generative AI is charged according to usage, so processing large amounts of data can be expensive. Although local LLM requires an initial cost, it reduces running costs thereafter, leading to cost savings in the long run.
• Greater customizability: Local LLM allows fine-tuning (additional learning) with your own data, allowing you to build AI specialized for specific tasks or industries. This allows you to obtain more accurate output.
• Offline use: Local LLM can be used even when not connected to the Internet, so it can be used safely even in places with unstable network environments or when working with data that contains confidential information.

4. Advantages and disadvantages of introducing local LLM

The advantages and disadvantages of introducing a local LLM are as follows:

merit

• Enhanced security
• Cost reduction
• Greater customizability
• Offline use

Disadvantages

• High initial cost
• Specialized knowledge may be required for installation and operation
• Need to keep up with the latest AI model updates

5. Local LLM Implementation Case Studies

Local LLMs are being increasingly introduced in a variety of industries.
Here we will introduce some specific implementation examples.

• Medical Institutions: Local LLMs, which can be used safely from a security standpoint, are gaining attention in medical institutions that handle confidential information such as patients' medical records and charts. For example, by inputting patients' symptoms and test results into a local LLM, it is possible to suggest appropriate treatments and medications, assist with diagnosis, and more.
• Financial Institutions: Financial institutions handle confidential information such as customer information and transaction history, so security is of paramount importance. By utilizing local LLM, it is possible to improve business efficiency while ensuring security by automating customer support, detecting fraud, and analyzing risks.
• Manufacturing: In the manufacturing industry, security measures are essential because confidential information such as product blueprints and manufacturing processes are handled. By introducing local LLM, it is possible to process confidential information in an in-house environment, leading to improved work efficiency and quality, such as design automation, quality control, and failure prediction.

6. Digirise's Corporate Implementation Support Program

As a leading generative AI company in Japan, Digirise offers a unique program to help companies introduce local LLMs.

Features of Digirise's implementation support program

• Extensive implementation track recordDigirise has supported the introduction of generative AI to over 40 companies, including major corporations such as GMO Internet Group, Persol Group, and Google Japan.
  We will utilize our track record and know-how to help you implement local LLM that best suits your needs.
• One-stop service: We provide one-stop support from selecting a local LLM to implementing, operating and training.
  You can proceed with the implementation with confidence even if you do not have specialized knowledge.
• Thorough security measures: We implement thorough security measures so that companies can use local LLM with peace of mind.
  Even data containing confidential or personal information can be processed safely.

Contents of the Digirise introduction support program

• Step 1Startup meeting (understanding current issues, goals, and what you want to achieve)
• Step 2: Self-study video content (LMS) (30 video content available for viewing and learning at your own pace)
• Step 3: A total of 4 comprehensive workshops (group/online training) (learning case studies/AI usage exercises/latest AI tools/corporate training programs/individual follow-up)
• Step 4: Customer support management (Proposing optimal models and implementation methods for each individual's training/Individual Q&A)
• Step 5: After-follow meetings (after-follow meetings (goal achievement follow-up/problem-solving support/improvement)

7. Summary: Accelerate AI adoption in Japanese companies with a local LLM!

Local LLM is a groundbreaking technology that offers many advantages, including security, cost, customizability, and offline use.
By utilizing Digirise's implementation support program, companies can implement local LLM with peace of mind and expect to achieve a variety of results, including improved business efficiency, increased productivity, and new business creation.

The local LLM will be a great opportunity for Japanese companies to accelerate their use of AI.
Why not take this opportunity to consider introducing a local LLM?

Q&A

Q1: What skills and knowledge are required to implement a local LLM?

A: To implement the local LLM, you will need basic IT knowledge as well as knowledge of programming languages such as Python.
However, rest assured that Digirise's implementation support program will provide thorough support through video content, workshops, chat support, and more, so that you can implement and operate the system even without specialized knowledge.

Q2: How much does it cost to implement a local LLM?

A: The cost of implementing a local LLM varies depending on the size of the model, the implementation method, and the level of customization required.
At Digirise, we will propose the best plan to suit your needs, so please feel free to contact us.
It is also possible to reduce costs by taking advantage of subsidies for IT implementation.

Q3: What kind of work can the Local LLM be used for?

A: Local LLM can be used for a variety of tasks, including text generation, translation, summarization, question answering, and data analysis.
For example, it is used for a wide range of purposes, such as automating customer support, creating marketing materials, translating internal documents, and streamlining market research.
It is also possible to build AI specialized for specific tasks by fine-tuning it using your own company's data.

Q4: How long will the Local LLM take to implement?

A: The implementation period for a local LLM varies depending on the size of the model, the implementation method, and the level of customization required, but typically ranges from a few weeks to a few months.
Digirise will assist you in rapid implementation according to your situation.

Q5: Is it difficult to operate a local LLM?

A: Compared to cloud-based generative AI, operating a local LLM requires some specialized knowledge, such as managing servers and networks.
However, Digirise also provides operational support after implementation, providing thorough support to ensure that customers can use local LLM with peace of mind.

Q6: Which companies should consider implementing a local LLM?

A: The introduction of the local LLM is recommended for the following companies:

• Companies that handle data including confidential information and personal information: Local LLM allows data to be processed within your company's environment, significantly reducing security risks.
• Companies that process large amounts of data: Local LLM has lower running costs than cloud-based generative AI, so cost savings can be expected.
• Companies that want to build AI specialized for specific tasks or industries: Local LLM allows you to fine-tune your own data, allowing you to build more accurate AI.

Q7: Can I get advice on implementing local LLM?

A: Of course. Digirise offers free consultations regarding the implementation of local LLM.
Please feel free to contact us.