What Are the Advantages of a Local LLM? Five Benefits of Running AI Inside the Company
The biggest advantage of a local LLM is that the company decides where its data goes: the model runs on company hardware, so questions, documents, and answers are processed inside the company instead of being sent to an outside model provider. There are four more benefits: it works offline, the company decides when to change model versions, usage logs stay in the company's hands, and use can be separated by confidentiality level. Each benefit has conditions, though: outbound connections must be turned off, someone must maintain the system, and the drawbacks must be weighed too.
1. Five benefits
| Benefit | Why | Condition |
|---|---|---|
| 1. Data does not have to leave the company | The official FAQ of a common local-model tool states that when you run locally, its developer does not see your prompts or data; only when you use its cloud models are prompts and responses sent to its service. With hardware in Taiwan, data can also stay in Taiwan; Article 21 of Taiwan's Personal Data Protection Act lets the authority restrict a non-government entity's international transfer of personal data in cases such as major national interests or inadequate data protection laws in the receiving country. | Turn off the tool's cloud features; check external channels such as LINE or email one by one, because messages through them still leave the company; include backup, monitoring, and update services located abroad. |
| 2. Works offline | Once a model is downloaded to the machine, inference needs no network, so it can run in an environment with no outside connection. | Updates are downloaded and verified on a connected machine first, then moved in. See Can a local LLM work offline? |
| 3. The company decides the version | The model files sit on company hardware, so the company decides when to change versions and can verify a new one with its own test questions first. | Some local tools' desktop apps download software updates automatically; switch to manual updates and pinned versions. |
| 4. Logs stay in the company | Who asked what and which documents the AI read are stored on company hardware. Article 12 of the Enforcement Rules of Taiwan's Personal Data Protection Act lists "usage records, trail data and preservation of evidence" as one of the security measures that may be adopted. | Logs contain personal data too, so set access rights and retention periods. |
| 5. Use by confidentiality level | Confidential and personal data can be processed on-premises while general work uses the cloud. Point 4 of the reference guidelines for the Executive Yuan treats closed, on-premises deployment as an option for use "according to confidentiality level" (see the next section). | Set data classification rules first, and confirm the on-premises environment is secure. |
2. How Taiwan's public sector views "on-premises"
The Reference Guidelines for the Use of Generative AI by the Executive Yuan and Its Agencies, drafted by the National Science and Technology Council, reported to the Executive Yuan Council on August 31, 2023, and issued to agencies for reference, say in point 4 that staff must not give generative AI information that must be kept confidential, personal information, or information not approved for release. However, a closed, on-premises generative AI model may be used according to confidentiality level after the security of the system environment has been confirmed.
These guidelines bind government agencies, not private companies. But they make one point clear: for confidential data, "sent outside for processing" and "processed in your own environment" are treated as two different situations. And even on-premises, the environment must be confirmed secure first; installing it is not enough.
3. Three things that must be true first
- Confirm nothing is going out. On-premises does not mean every connection stays local; cloud models, messaging platforms, and external services all send requests out. For where data goes step by step, see When AI searches company documents, where does the data go?
- Someone is responsible for maintenance. NIST SP 800-40 Rev. 4 frames patching as preventive maintenance, a necessary, ongoing part of using technology. Local models, operating systems, and surrounding software all need updates.
- The hardware can run it. Without enough GPU memory, the model runs slowly or a smaller model must be used. See What hardware does a company need to run a local LLM?
Local LLMs also have drawbacks: upfront hardware cost, capability that usually trails the latest cloud models, and the need for maintenance. Whether one fits should be decided by weighing the benefits and drawbacks together.
Think BIG's two deployment options
Think BIG (Think BIG Make it Real Co., Ltd.) is an enterprise AI Agent deployment provider headquartered in Taiwan. Starting from protecting company data, we build AI Agents inside the client's own environment (company hardware, or a cloud server under the company's account). There are two deployment options: with the on-premises version, models and documents are processed on company hardware; with the cloud-assisted version, employees' questions and the document passages the system retrieves are sent to the model provider for processing.
- Data flow listed before building: where the application, model, knowledge-base index, source documents, logs, and backups each sit is listed item by item.
- External channels confirmed one by one: on-premises does not mean every connection stays local. If an external channel such as LINE is added, messages through that channel pass through that service; we list this for the client before building.
- Pinned versions, no automatic updates: third-party packages are reviewed for source and license, pinned, and not auto-updated. Each system goes through a security review before delivery; if it does not pass, it is not delivered.
See On-premises AI and data sovereignty and Trust.
The actual scope of Think BIG's services follows what both parties confirm.
FAQ
- Q1: What are the advantages of a local LLM?
- The biggest advantage is that the company decides where its data goes: questions, documents, and answers are processed on company hardware. It also works offline, the company decides when to change model versions, usage logs stay in-house, and use can be separated by confidentiality level.
- Q2: Does a local LLM mean data can never leak?
- No. On-premises does not mean every connection stays local. If the tool's cloud features are on, or messages pass through external channels such as LINE or email, that content still leaves. Check outbound connections one by one and set permissions and logging.
- Q3: Can Taiwan government agencies use a local LLM for confidential work?
- Point 4 of the National Science and Technology Council's reference guidelines for the Executive Yuan says staff must not give generative AI confidential, personal, or unreleased information, but a closed, on-premises generative AI model may be used according to confidentiality level after the security of the system environment has been confirmed.
- Q4: Can a local LLM run fully offline?
- Yes. Once the model is downloaded, inference needs no network. Turn off the tool's cloud features, and download and verify updates on a connected machine before moving them into the offline environment.
- Executive Yuan Council agenda item: Reference Guidelines for the Use of Generative AI by the Executive Yuan and Its Agencies (2023-08-31): www.ey.gov.tw
- National Science and Technology Council press release with the full guideline text, point 4 (2023-08-31): www.ey.gov.tw
- Personal Data Protection Act, Article 21 (international transfer), Laws & Regulations Database of Taiwan: law.moj.gov.tw
- Enforcement Rules of the Personal Data Protection Act, Article 12 (security measures), Laws & Regulations Database of Taiwan: law.moj.gov.tw
- NIST SP 800-40 Rev. 4, Guide to Enterprise Patch Management Planning: Preventive Maintenance for Technology (2022): nvlpubs.nist.gov
- Official FAQ of a local-model tool (no prompts sent when running locally, disabling cloud features, desktop auto-updates): docs.ollama.com
- Think BIG, RAG knowledge Q&A (on-premises does not mean every connection stays local): thinkbigtw.com
This article summarizes public official documents, laws, and security standards to help companies evaluate AI adoption. It is not legal advice. For Taiwan law, the latest text on the Laws & Regulations Database prevails; external documents, prices, and specifications follow their original sources. The actual scope of Think BIG’s services depends on what both parties confirm.