# Vidur: A Large-Scale Simulation Framework For LLM Inference

arXiv: https://arxiv.org/abs/2405.05465 · HTML: https://mlsanity.com/paper/2405.05465/

## Key facts

- Cited by 117 papers in the map (data through 14 Oct 2026).
- Builds on 15 of its 30 references that are in the map.
- First posted on arXiv 8 May 2024.

## Builds on (most cited first, 15 of 15)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Efficient Memory Management for Large Language Model Serving with PagedAttention](https://mlsanity.com/paper/2309.06180/) (2023)
- [Megatron-LM: Training Multi-Billion Parameter Language Models Using Model Parallelism](https://mlsanity.com/paper/1909.08053/) (2019)
- [DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Serving](https://mlsanity.com/paper/2401.09670/) (2024)
- [Splitwise: Efficient generative LLM inference using phase splitting](https://mlsanity.com/paper/2311.18677/) (2023)
- [Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Serve](https://mlsanity.com/paper/2403.02310/) (2024)
- [cuDNN: Efficient Primitives for Deep Learning](https://mlsanity.com/paper/1410.0759/) (2014)
- [SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills](https://mlsanity.com/paper/2308.16369/) (2023)
- [TeraPipe: Token-Level Pipeline Parallelism for Training Large-Scale Language Models](https://mlsanity.com/paper/2102.07988/) (2021)
- [SPEED: Speculative Pipelined Execution for Efficient Decoding](https://mlsanity.com/paper/2310.12072/) (2023)
- [Daydream: Accurately Estimating the Efficacy of Optimizations for DNN Training](https://mlsanity.com/paper/2006.03318/) (2020)
- [Building a Performance Model for Deep Learning Recommendation Model Training on GPUs](https://mlsanity.com/paper/2201.07821/) (2022)
- [Proteus: Simulating the Performance of Distributed DNN Training](https://mlsanity.com/paper/2306.02267/) (2023)
- [Discourse Centric Evaluation of Machine Translation with a Densely Annotated Parallel Corpus](https://mlsanity.com/paper/2305.11142/) (2023)

## Built on by (most cited first, 30 of 117)

- [On Large Language Model Continual Unlearning](https://mlsanity.com/paper/2407.10223/) (2024)
- [POD-Attention: Unlocking Full Prefill-Decode Overlap for Faster LLM Inference](https://mlsanity.com/paper/2410.18038/) (2024)
- [LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale](https://mlsanity.com/paper/2408.05499/) (2024)
- [ServeGen: Workload Characterization and Generation of Large Language Model Serving in Production](https://mlsanity.com/paper/2505.09999/) (2025)
- [Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs](https://mlsanity.com/paper/2502.00722/) (2025)
- [Online Scheduling for LLM Inference with KV Cache Constraints](https://mlsanity.com/paper/2502.07115/) (2025)
- [Optimizing LLM Inference: Fluid-Guided Online Scheduling with Memory Constraints](https://mlsanity.com/paper/2504.11320/) (2025)
- [How Fast Can I Run My VLA? Demystifying VLA Inference Performance with VLA-Perf](https://mlsanity.com/paper/2602.18397/) (2026)
- [Very Large-Scale Multi-Agent Simulation in AgentScope](https://mlsanity.com/paper/2407.17789/) (2024)
- [Throughput-Optimal Scheduling Algorithms for LLM Inference and AI Agents](https://mlsanity.com/paper/2504.07347/) (2025)
- [Lynx: Enabling Efficient MoE Inference through Dynamic Batch-Aware Expert Selection](https://mlsanity.com/paper/2411.08982/) (2024)
- [Taming the Titans: A Survey of Efficient LLM Inference Serving](https://mlsanity.com/paper/2504.19720/) (2025)
- [Glia: A Human-Inspired AI for Automated Systems Design and Optimization](https://mlsanity.com/paper/2510.27176/) (2025)
- [APEX: An Extensible and Dynamism-Aware Simulator for Automated Parallel Execution in LLM Serving](https://mlsanity.com/paper/2411.17651/) (2024)
- [No Request Left Behind: Tackling Heterogeneity in Long-Context LLM Inference with Medha](https://mlsanity.com/paper/2409.17264/) (2024)
- [Demystifying AI Platform Design for Distributed Inference of Next-Generation LLM models](https://mlsanity.com/paper/2406.01698/) (2024)
- [Etalon: Holistic Performance Evaluation Framework for LLM Inference Systems](https://mlsanity.com/paper/2407.07000/) (2024)
- [On Evaluating Performance of LLM Inference Serving Systems](https://mlsanity.com/paper/2507.09019/) (2025)
- [Adaptively Robust LLM Inference Optimization under Prediction Uncertainty](https://mlsanity.com/paper/2508.14544/) (2025)
- [AIConfigurator: Lightning-Fast Configuration Optimization for Multi-Framework LLM Serving](https://mlsanity.com/paper/2601.06288/) (2026)
- [LLMServingSim 2.0: A Unified Simulator for Heterogeneous and Disaggregated LLM Serving Infrastructure](https://mlsanity.com/paper/2602.23036/) (2026)
- [Privacy-preserved LLM Cascade via CoT-enhanced Policy Learning](https://mlsanity.com/paper/2410.08014/) (2024)
- [KVDirect: Distributed Disaggregated LLM Inference](https://mlsanity.com/paper/2501.14743/) (2024)
- [Rethinking Key-Value Cache Compression Techniques for Large Language Model Serving](https://mlsanity.com/paper/2503.24000/) (2025)
- [Janus: Disaggregating Attention and Experts for Scalable MoE Inference](https://mlsanity.com/paper/2512.13525/) (2025)
- [DualMap: Enabling Both Cache Affinity and Load Balancing for Distributed LLM Serving](https://mlsanity.com/paper/2602.06502/) (2026)
- [Niyama : Breaking the Silos of LLM Inference Serving](https://mlsanity.com/paper/2503.22562/) (2025)
- [Forecasting LLM Inference Performance via Hardware-Agnostic Analytical Modeling](https://mlsanity.com/paper/2508.00904/) (2025)
- [LLM Serving Optimization with Variable Prefill and Decode Lengths](https://mlsanity.com/paper/2508.06133/) (2025)
- [Improving Coherence and Persistence in Agentic AI for System Optimization](https://mlsanity.com/paper/2603.21321/) (2026)

## Abstract (arXiv)

Optimizing the deployment of Large language models (LLMs) is expensive today since it requires experimentally running an application workload against an LLM implementation while exploring large configuration space formed by system knobs such as parallelization strategies, batching techniques, and scheduling policies. To address this challenge, we present Vidur - a large-scale, high-fidelity, easily-extensible simulation framework for LLM inference performance. Vidur models the performance of LLM operators using a combination of experimental profiling and predictive modeling, and evaluates the end-to-end inference performance for different workloads by estimating several metrics of interest such as latency and throughput. We validate the fidelity of Vidur on several LLMs and show that it estimates inference latency with less than 9% error across the range. Further, we present Vidur-Search, a configuration search tool that helps optimize LLM deployment. Vidur-Search uses Vidur to automatically identify the most cost-effective deployment configuration that meets application performance constraints. For example, Vidur-Search finds the best deployment configuration for LLaMA2-70B in one hour on a CPU machine, in contrast to a deployment-based exploration which would require 42K GPU hours - costing ~218K dollars. Source code for Vidur is available at https://github.com/microsoft/vidur.