Intermediate Model Storage for Secure Local LLM Command Routing
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Solution Overview
Problem
The challenge of storing large language models (LLM) requires significant data and computing resources, and existing methods of transmitting interaction information between LLM and users over the Internet pose a risk of personal information or commercial confidentiality leakage.
Innovation Solution
An intermediate storage device with a model storage unit, data storage unit, AI selecting unit, and access control unit is provided, enabling local invocation and selection of language models based on application scenarios, with an access control unit determining command types and facilitating model operation or data access commands.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If language models are stored and processed locally in an intermediate storage device, then information security is improved and response time is reduced, but device complexity and storage requirements increase
Solution Approach 1:
The patent introduces an intermediate storage device as a mediator between the user terminal and the language model. This intermediate device stores multiple language models locally and manages their invocation, thereby preventing direct transmission of sensitive information over the network while reducing response time. The intermediate storage device acts as a buffer that resolves the contradiction between security and efficiency.
Solution Approach 2:
The system is segmented into multiple components: the user terminal, the intermediate storage device with model storage unit, and the upper-layer operating system. By dividing the architecture and placing language models in a dedicated intermediate storage device rather than requiring full local deployment or direct cloud access, the system achieves security without excessive complexity at any single point.
2Adaptability or versatility
If multiple language models are stored locally for different application scenarios, then service versatility is improved, but storage space and resource requirements increase
Solution Approach 1:
The intermediate storage device is designed with multi-functionality to store and manage multiple different language models (e.g., translation models, dialogue models, text generation models) in a single unified structure. This universal storage approach allows the system to handle various application scenarios without requiring separate storage systems for each model type, optimizing space utilization while maintaining versatility.
3Use of energy by moving object
If language models are transmitted over the Internet for processing, then device resource requirements are reduced, but information leakage risks increase
Solution Approach 1:
The intermediate storage device serves as a secure intermediary that locally stores language models and handles processing requests without requiring continuous network transmission. Sensitive information remains on the local device while still benefiting from powerful language model capabilities, thus reducing information leakage risks while maintaining reasonable resource consumption through selective local processing.
Data Source
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AI summary
An intermediate storage device (300) a computer (10) system, and a computer (10) command preprocessing method are provided. The intermediate storage device (300) includes a model storage unit (310), a data storage unit (320), an AI selecting unit (330), and an access control unit (340). The model storage unit (310) stores a plurality of language models. The data storage unit (320) stores a plurality of file data. The AI selecting unit (330) selects a language model based a model operation command (331), and the language model executes the model operation command (331) and generates a generation result. The access control unit (340) connect to the data storage unit (320) and the model storage unit (310). The access control unit (340) determines whether an input command (210) is a model operation command (331) or a data access command (332). The input command (210) is the model operation command (331), the access control unit (340) forwards the model operation command (331) to the AI selecting unit (330) and send the output data (334) to the access control unit (340).