Multi-Level AI Architecture for Scalable Generative Models
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current AI generative models and Large Language Models (LLMs) require significant supercomputing efforts, leading to high computational costs, slow response times, and limited scalability and expandability.
Innovation Solution
The system employs a multi-level generative AI and LLM architecture that utilizes derived requests, multiple h-LLMs with varying levels of accuracy, and a distributed architecture to efficiently process client requests, assign tasks, and combine results for improved performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If current AI generative models and LLMs are used, then language processing and content generation capabilities are achieved, but computational costs are high and response times are slow
Solution Approach 1:
The patent segments the monolithic LLM into multiple smaller specialized models (h-LLMs) organized in a hierarchical structure. Each h-LLM is trained on specific datasets for particular tasks, allowing the system to process requests in parallel and reduce overall computational burden while maintaining response quality.
Solution Approach 2:
The patent introduces a hierarchical dimension to the model architecture, organizing h-LLMs into multiple levels where higher-level models coordinate lower-level specialized models. This dimensional transformation enables more efficient resource allocation and reduces computational costs through distributed processing.
2Adaptability or versatility
If current AI generative models and LLMs are used, then content generation capability is achieved, but scalability and expandability are limited
Solution Approach 1:
The patent creates a universal hierarchical framework where h-LLMs can be dynamically allocated to different tasks based on request requirements. The system can handle diverse generative tasks (text, code, images, video) using the same architectural pattern, enabling easy scaling and expansion without redesigning the core system.
Solution Approach 2:
The patent implements a dynamic request routing system that assigns incoming requests to appropriate h-LLMs based on task characteristics, model availability, and performance metrics. This dynamic allocation enables the system to adapt to changing workloads and scale efficiently as new models are added.
Data Source
AI summary
A method of generating responses responsive to input prompts including receiving a multimodal input prompt, the multi-modal input prompt comprising a first input prompt mode and a second input prompt mode, generating first and second derived prompts responsive to the first and second input prompt modes, transmitting the first and second derived prompts to first and second h-LLMs that are trained using data having a mode that is the same of the respective input prompt modes, receiving one or more first and second results from the first and second h-LLMs, generating at least one combined result from the one or more first and second results, and transmitting the combined result to the user.