Large Language Models (LLMs) such as GPT-4 and LLaMA2-70B enable various applications in natural language processing. However, their deployment is challenged by high costs and the need to fine-tune many system settings to achieve optimal performance. Deploying these models involves a complex selection process among various system configurations and traditionally requires expensive and time-consuming experimentation.
Researchers from the Georgia Institute of Technology and Microsoft Research India developed Vidur, a simulation framework designed specifically for LLM inference. Vidur uses a mixture of experimental data and predictive modeling to simulate the performance of LLMs under different configurations. This approach allows for assessing key performance metrics such as latency and throughput without going through costly physical trials.
A major component of Vidur is Vidur-Search, a configuration search tool that automates the exploration of deployment configurations. This tool can pinpoint the most cost-effective settings that meet predefined performance standards. For instance, Vidur-Search found an optimal setup for the LLaMA2-70B model on a CPU platform in just one hour, a task that would traditionally require significant GPU resources.
Beyond this, Vidur can evaluate various LLMs across different hardware setups and cluster configurations. Its prediction accuracy rate for inference latency is less than 9%. Also, Vidur introduces Vidur-Bench, a benchmark suite that helps with comprehensive performance evaluations using diverse workload patterns and system configurations.
Vidur has demonstrated a significant reduction in the cost of deploying LLMs in practice. By using Vidur-Search in simulation environments, potential costs that could exceed $200,000 in real-world scenarios can be dramatically reduced. This cost-effectiveness doesn’t compromise the accuracy or relevance of the results, ensuring that performance optimizations are both feasible and effective.
In conclusion, Vidur’s simulation framework addresses the high costs and complexity of deploying large language models by adopting an innovative combination of experimental profiling and predictive modeling. This method enables accurate simulation of LLM performance across various configurations, significantly reducing the need for expensive physical testing. With less than 9% error in latency predictions and substantial reductions in GPU hours and related costs, Vidur is a pivotal tool for simplifying LLM deployment in a practical and cost-effective manner.
