MaaS Request Throttling With Cross-Model GPU Metric Standardization
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Solution Overview
Problem
Existing MaaS platforms face challenges in dynamically reallocating GPU resources among different large language models (LLMs) due to the inability to compare latency and resource utilization metrics across models with different tokenization schemes, leading to inefficiencies in need-based resource allocation.
Innovation Solution
A platform-level metric standardizer converts token-based metrics into model-agnostic metrics using model-specific benchmark metrics to facilitate cross-model comparisons and enable dynamic resource allocation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If token-based metrics are used for each LLM, then model-specific performance can be measured, but cross-model comparison becomes impossible due to different tokenization schemes
Solution Approach 1:
The patent introduces a platform-level metric standardizer as an intermediary component that receives token-based metrics from multiple LLMs with different tokenization schemes and converts them into standardized model-agnostic metrics. This mediator enables cross-model comparison by translating diverse model-specific measurements into a common framework without losing the precision of individual model measurements.
2Reliability
If GPU resources are statically allocated to each model pool, then resource isolation is ensured, but resource utilization efficiency decreases when demand varies
Solution Approach 1:
The patent implements dynamic resource allocation where the platform metric standardizer continuously monitors standardized metrics from multiple model pools and automatically reallocates GPU resources based on current demand. This dynamic approach maintains resource isolation through controlled allocation while improving overall utilization efficiency by shifting resources to high-demand models and releasing them from low-demand models.
3Productivity
If model-specific metrics are used for resource allocation, then each model's needs are optimized, but platform-wide resource optimization becomes impossible
Solution Approach 1:
The patent creates a universal metric framework where the platform-level metric standardizer produces model-agnostic metrics that serve multiple functions: they preserve model-specific performance characteristics for individual optimization while simultaneously enabling platform-wide resource allocation decisions. This multi-functional metric system allows the same standardized metrics to drive both model-specific and platform-wide resource optimization.
Data Source
AI summary
A model-as-a-service (MaaS) platform performs cross-model resources allocation from a shared pool of GPU resources based on model-agnostic metrics generated by a metric standardizer. The metric standardizer receives, from model providers, model-specific benchmark metrics that define relationships between resource utilization and token processing according to the different model-specific tokenization schemes; receives, from one or more MaaS components, token-based job metrics pertaining to LLM processing tasks; and determines, based on the model-specific benchmark metrics and token-based job metrics, the model-agnostic metrics for multiple model pools executing instances of different large language models (LLMs) that generate and process text according to different model-specific tokenization schemes. The MaaS platform further includes one or more resource allocation components that dynamically reallocates resources of the shared pool based on the model-agnostic metric.