Multi-Level AI Supercomputer Architecture for Faster LLM Inference
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Solution Overview
Problem
Current AI generative models and large language models (LLMs) require significant supercomputing efforts to improve response times, accuracy, and reduce computational load, limiting their scalability and expandability.
Innovation Solution
A multi-level generative AI system utilizing derived requests, multiple h-LLMs with varying levels of accuracy, and a distributed architecture to efficiently process and refine tasks, incorporating techniques like bagging, boosting, and merging/fusing h-LLMs to enhance precision and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional single-level LLM architecture is used, then model simplicity is maintained, but computational load is high and response time is slow
Solution Approach 1:
The system divides the LLM processing into multiple hierarchical levels: Level-1 h-LLMs handle specific task categories, Level-2 h-LLMs handle broader categories, and Level-3 h-LLMs handle general tasks. This segmentation allows computational work to be distributed across multiple specialized models rather than requiring a single large model to handle all tasks, reducing overall computational load while improving response time through specialized processing.
Solution Approach 2:
The patent introduces a hierarchical dimension to the traditional single-level architecture by organizing h-LLMs into multiple levels (Level-1, Level-2, Level-3) with different scopes of responsibility. This dimensional transformation from a flat single-level structure to a multi-level hierarchy enables more efficient task routing and reduces computational requirements at each level.
2Adaptability or versatility
If multiple h-LLMs with varying accuracy levels are deployed, then task handling capability is improved, but system complexity increases
Solution Approach 1:
The system segments h-LLMs into different levels based on their task handling scope and accuracy requirements. Level-1 h-LLMs handle specific task categories with high accuracy, Level-2 h-LLMs handle broader categories, and Level-3 h-LLMs handle general tasks. This segmentation provides adaptability for different task types while managing complexity through structured organization and clear hierarchical relationships.
3Productivity
If workload is distributed across multiple h-LLMs, then computational efficiency is improved, but coordination overhead increases
Solution Approach 1:
The system performs preliminary action by pre-organizing h-LLMs into a hierarchical structure with defined levels and categories before task execution. Each h-LLM is pre-assigned to specific task categories and levels, establishing clear routing rules in advance. This preliminary organization reduces coordination overhead during runtime by eliminating the need for complex real-time decision-making about task allocation.
Data Source
AI summary
Systems and methods for in-memory processing of h-LLM data including receiving an input data stream, operating a data receiver operable to divide the input data stream into a plurality of data batches, processing the plurality of data batches using a processing layer, the processing layer comprising a plurality of h-LLMs operating at least partially in volatile memory, and producing a plurality of processed data batches from an output of the processing layer.