Loading Now

Summary of Vidur: a Large-scale Simulation Framework For Llm Inference, by Amey Agrawal et al.


Vidur: A Large-Scale Simulation Framework For LLM Inference

by Amey Agrawal, Nitin Kedia, Jayashree Mohan, Ashish Panwar, Nipun Kwatra, Bhargav Gulavani, Ramachandran Ramjee, Alexey Tumanov

First submitted to arxiv on: 8 May 2024

Categories

  • Main: Machine Learning (cs.LG)
  • Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL)

     Abstract of paper      PDF of paper


GrooveSquid.com Paper Summaries

GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!

Summary difficulty Written by Summary
High Paper authors High Difficulty Summary
Read the original abstract here
Medium GrooveSquid.com (original content) Medium Difficulty Summary
A novel approach to optimizing the deployment of Large Language Models (LLMs) is presented in this paper, which requires exploring a vast configuration space formed by system knobs such as parallelization strategies, batching techniques, and scheduling policies. To address this challenge, the authors introduce Vidur, a large-scale simulation framework for LLM inference performance that combines experimental profiling and predictive modeling to estimate metrics like latency and throughput. The authors validate the fidelity of Vidur on several LLMs, demonstrating an error of less than 9% in estimating inference latency across the range. Additionally, they propose Vidur-Search, a configuration search tool that uses Vidur to automatically identify the most cost-effective deployment configuration meeting application performance constraints. This paper demonstrates the potential of Vidur and Vidur-Search to significantly reduce the time and cost required for LLM deployment optimization.
Low GrooveSquid.com (original content) Low Difficulty Summary
Large Language Models (LLMs) are powerful tools, but deploying them can be expensive and time-consuming. Researchers have created a new way to simulate how well an LLM will perform in different situations, which can help make the process faster and cheaper. This method, called Vidur, uses both experimental data and predictions to estimate things like how long it takes for the LLM to complete a task (latency) and how many tasks it can do per second (throughput). The researchers tested Vidur on several different LLMs and found that it was very accurate. They also created a tool called Vidur-Search, which uses Vidur to find the best way to deploy an LLM based on what you want to use it for.

Keywords

» Artificial intelligence  » Inference  » Optimization