Multi-Level LLM Architecture for Accuracy and Compute Balance
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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 through techniques like bagging, boosting, and merging/fusing models, along with a local database for future request categorization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional single-model LLM approaches are used to improve accuracy and response times, then model performance may be enhanced, but computational load and resource requirements increase significantly
Solution Approach 1:
The system segments the LLM inference process into multiple hierarchical levels (Level-0 through Level-3), where each level handles different complexity tiers of requests. Simple requests are processed at lower levels with smaller models, while only complex requests escalate to higher levels with larger models, distributing computational load across multiple specialized components rather than concentrating it in a single large model.
Solution Approach 2:
The system dynamically routes requests to appropriate model levels based on real-time analysis of request characteristics, complexity, and current system state. The router selectively escalates or de-escalates requests between levels, creating a dynamic adaptation mechanism that optimizes computational resource usage while maintaining accuracy for complex tasks.
2Adaptability or versatility
If larger and more complex LLM models are deployed to handle diverse tasks, then task capability and accuracy improve, but system complexity and resource requirements increase
Solution Approach 1:
The system divides the model ensemble into hierarchical levels (Level-0 to Level-3) with progressively increasing capability and size. Each level specializes in handling specific task complexity ranges, allowing the system to achieve high adaptability through model diversity while managing complexity through structured organization and selective deployment of only necessary model levels.
Solution Approach 2:
The system adds a hierarchical dimension to model deployment, organizing models not just by size but by functional capability tiers. This dimensional organization allows diverse task capabilities to be achieved through vertical stratification, where each level builds upon previous levels, rather than requiring all models to coexist at the same complexity level.
3Measurement precision
If multiple LLM models are used in parallel to improve accuracy through ensemble methods, then prediction accuracy improves, but computational overhead and response time increase
Solution Approach 1:
The system segments the ensemble computation into hierarchical levels where simpler models (lower levels) process the majority of requests quickly, providing baseline accuracy. Only when higher-level models are needed for complex requests does the system engage additional computational layers, thereby maintaining prediction accuracy for difficult tasks while minimizing response time for common requests.
Solution Approach 2:
The system applies partial ensemble action by selectively engaging only the necessary model levels required for each specific request's complexity. Rather than always deploying all models in parallel, the system uses partial computation at lower levels and escalates to full ensemble action only when needed, optimizing the trade-off between accuracy and response time.
Data Source
AI summary
A system for processing LLM operations including a memory module, a data receiver to receive an input data stream and break the input data stream into batches of input data, a processing layer to process the batches of input data in the one or more memory modules and having LLMs to process the batches of input data and generate corresponding batches of processed output data and an output broker to cache results from the plurality of LLMs in a results cache, assign priority scores to the cached results, rank the cached results based on their priority scores, and provide ranked results responsive to subsequent similar input data received by the data receiver from the results cache.