LLM Load Benchmarking with Adaptive Workload Profiles
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Solution Overview
Problem
Existing cloud computing platforms lack an effective framework for load testing and performance benchmarking of large language transformer-based models, leading to unpredictable performance and increased operational costs due to the complexity and dynamic nature of these models.
Innovation Solution
Implementing load profiles to generate representative workloads that simulate various workload contexts, allowing for consistent and repeatable performance testing and benchmarking of large language models, including latency and data throughput analysis, and utilizing a feedback loop to iteratively adjust these profiles based on performance metrics.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If load testing and performance benchmarking are implemented for large language models, then performance measurement accuracy and reliability are improved, but system complexity and testing infrastructure requirements increase
Solution Approach 1:
The patent introduces a performance benchmarking system that acts as an intermediary between the large language model and the testing infrastructure. This system includes components such as a workload generator, performance monitor, and analysis module that mediate the complex interactions between diverse workload contexts and model performance metrics, thereby improving measurement accuracy while managing infrastructure complexity through structured intermediation.
Solution Approach 2:
The system dynamically adjusts testing parameters such as workload intensity, input size, temperature settings, and top-p values to optimize performance measurements across different contexts. By systematically varying these parameters and observing their impact on model performance, the system achieves comprehensive and accurate benchmarking without requiring overly complex fixed infrastructure.
2Measurement precision
If diverse workload contexts are tested to ensure comprehensive performance evaluation, then measurement completeness is improved, but testing time and resource consumption increase
Solution Approach 1:
The system performs preliminary analysis of the large language model's characteristics and usage patterns to pre-configure appropriate workload contexts and parameters before actual benchmarking begins. This preliminary setup includes identifying relevant workload types, setting appropriate parameter ranges, and preparing test scenarios based on expected use cases, thereby reducing the time required for comprehensive evaluation while maintaining measurement completeness.
Solution Approach 2:
The system implements a tiered benchmarking approach that tests a core set of essential workload contexts in detail while performing lighter testing on secondary contexts. This partial action strategy ensures that the most critical performance aspects are thoroughly evaluated while still providing adequate coverage of diverse workload scenarios, balancing completeness with time efficiency.
3Loss of information
If multiple performance metrics are monitored simultaneously, then information completeness is improved, but data processing complexity and computational overhead increase
Solution Approach 1:
The performance monitoring system extracts and focuses on specific key performance indicators such as response time, tokens per second, and quality metrics that are most relevant to large language model evaluation. By selectively extracting only the most critical metrics rather than monitoring all possible performance parameters, the system maintains comprehensive performance information while reducing computational overhead and energy consumption associated with processing excessive data.
Solution Approach 2:
The system combines multiple performance metrics into composite performance scores and aggregated reports that preserve essential information while reducing data volume. By merging related metrics such as combining various quality measures into an overall quality score or aggregating response time data across different workloads, the system maintains information completeness while lowering the computational burden of processing and analyzing individual metric values.
Data Source
AI summary
The techniques disclosed herein enable systems to perform repeatable and iterative load testing and performance benchmarking for artificial intelligence models deployed in a cloud computing environment. This is achieved by utilizing load profiles and representative workloads generated based on the load profiles to evaluate an artificial intelligence model under various workload contexts. The representative workload is then executed by the artificial intelligence model utilizing available computing infrastructure. Performance metrics are extracted from the execution and analyzed to provide insight into various performance dynamics such as the relationship between latency and data throughput. In addition, load profiles and input datasets are dynamically adjusted to evaluate different scenarios and use cases enabling the system to automatically test the artificial intelligence model across diverse applications. Furthermore, by comparing various iterations of the artificial intelligence model, a quality gate can be constructed to enforce a consistent and high-quality user experience.