Enterprise GenAI Task Routing Across Heterogeneous Hardware
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Solution Overview
Problem
Existing GenAI systems face challenges in efficiently routing tasks to various enterprise hardware resources, leading to inefficiencies in performance, latency, and increased computational overhead due to factors like VRAM usage, sequential token generation, and hidden state preservation, which can result in unsustainable GPU demands and uneven load distribution.
Innovation Solution
The implementation of task performer circuitry that efficiently distributes GenAI tasks across local and remote hardware resources using user-controlled, algorithm-controlled, hybrid, and dual-path routing strategies, incorporating routing identifier, service identifier, parser, retriever, agent identifier, and evaluator circuitry to optimize resource allocation and scalability.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If GenAI tasks are routed to enterprise hardware resources, then task execution capability is improved, but computational overhead and GPU demands increase
Solution Approach 1:
The patent segments GenAI tasks into different types (e.g., text generation, image generation, analysis tasks) and routes them to different enterprise hardware resources based on task characteristics. This segmentation allows optimized resource utilization where each hardware component handles specific task types most efficiently, reducing overall computational overhead while maintaining execution capability.
Solution Approach 2:
The system dynamically changes routing parameters based on task requirements, hardware availability, and performance metrics. By adjusting routing decisions in real-time based on varying parameters such as task complexity, hardware load, and resource availability, the system optimizes the balance between execution capability and computational overhead.
2Productivity
If GenAI tasks are distributed across multiple hardware resources, then resource utilization efficiency is improved, but routing complexity increases
Solution Approach 1:
The patent introduces an intermediary routing system that acts as a mediator between GenAI tasks and enterprise hardware resources. This intermediary layer manages the complexity of routing decisions by implementing standardized protocols and algorithms that automatically match tasks to appropriate hardware, simplifying the overall system architecture while improving resource utilization efficiency.
Solution Approach 2:
The routing system implements dynamic adaptation to changing system conditions, automatically adjusting task distribution based on real-time hardware availability, load conditions, and performance metrics. This dynamic approach allows the system to maintain optimal resource utilization without requiring complex static routing configurations.
3Quantity of substance
If VRAM usage and hidden state preservation are optimized, then memory efficiency is improved, but task processing speed may be reduced
Solution Approach 1:
The system optimizes memory efficiency by dynamically adjusting parameters such as VRAM allocation, batch sizes, and hidden state preservation strategies based on task characteristics and hardware capabilities. This parameter optimization allows the system to achieve better memory efficiency while maintaining acceptable processing speeds through intelligent trade-off management.
Data Source
AI summary
Methods and apparatus disclosed herein introduce a comprehensive framework for distributing generative-AI workloads across diverse enterprise hardware. Multiple routing strategies disclosed herein include user-controlled, algorithm-controlled, hybrid, and dual-path routing with feedback to accommodate varying user expertise and resource availability. The routing logic is detailed for Question Answering (QA) tasks, Retrieval-Augmented Generation (RAG)-based tasks (e.g., document parsing and retrieval), and agent tasks, including evaluation of resource availability, model complexity, and content characteristics. User feedback can be obtained to continuously refine routing decisions through Large Language Model (LLM) based and traditional machine learning models. Methods and apparatus disclosed herein initiate routing decisions to maintain cost-efficiency, performance optimization, and accuracy across heterogeneous computing environments.