# Revati: Transparent GPU-Free Time-Warp Emulation for LLM Serving

arXiv: https://arxiv.org/abs/2601.00397 · HTML: https://mlsanity.com/paper/2601.00397/

## Key facts

- Cited by 5 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 1 Jan 2026.

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

- [Qwen3 Technical Report](https://mlsanity.com/paper/2505.09388/) (2025)
- [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)
- [Mooncake: A KVCache-centric Disaggregated Architecture for LLM Serving](https://mlsanity.com/paper/2407.00079/) (2024)
- [SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefills](https://mlsanity.com/paper/2308.16369/) (2023)
- [LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inference](https://mlsanity.com/paper/2510.09665/) (2025)
- [Vidur: A Large-Scale Simulation Framework For LLM Inference](https://mlsanity.com/paper/2405.05465/) (2024)
- [Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks](https://mlsanity.com/paper/1808.07412/) (2018)
- [LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scale](https://mlsanity.com/paper/2408.05499/) (2024)
- [Forecasting GPU Performance for Deep Learning Training and Inference](https://mlsanity.com/paper/2407.13853/) (2024)
- [APEX: An Extensible and Dynamism-Aware Simulator for Automated Parallel Execution in LLM Serving](https://mlsanity.com/paper/2411.17651/) (2024)
- [Beyond the Buzz: A Pragmatic Take on Inference Disaggregation](https://mlsanity.com/paper/2506.05508/) (2025)
- [TokenSim: Enabling Hardware and Software Exploration for Large Language Model Inference Systems](https://mlsanity.com/paper/2503.08415/) (2025)
- [LLMServingSim2.0: A Unified Simulator for Heterogeneous Hardware and Serving Techniques in LLM Infrastructure](https://mlsanity.com/paper/2511.07229/) (2025)

## Built on by (most cited first, 5 of 5)

- [Frontier: Towards Comprehensive and Accurate LLM Inference Simulation](https://mlsanity.com/paper/2605.21312/) (2026)
- [Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation](https://mlsanity.com/paper/2606.01725/) (2026)
- [AgentServeSim: Serving-System Simulation and Policy Search for LLM Agent Programs](https://mlsanity.com/paper/2606.09613/) (2026)
- [LLM-Emu: Native Runtime Emulation of LLM Inference via Profile-Driven Sampling](https://mlsanity.com/paper/2605.00616/) (2026)
- [Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation](https://mlsanity.com/paper/2605.07985/) (2026)

## Abstract (arXiv)

Deploying LLMs efficiently requires testing hundreds of serving configurations, but evaluating each one on a GPU cluster takes hours and costs thousands of dollars. Discrete-event simulators are faster and cheaper, but they require re-implementing the serving system's control logic -- a burden that compounds as frameworks evolve. We present Revati, a time-warp emulator that enables performance modeling by directly executing real serving system code at simulation-like speed. The system intercepts CUDA API calls to virtualize device management, allowing serving frameworks to run without physical GPUs. Instead of executing GPU kernels, it performs time jumps -- fast-forwarding virtual time by predicted kernel durations. We propose a coordination protocol that synchronizes these jumps across distributed processes while preserving causality. On vLLM and SGLang, Revati achieves less than 5% prediction error across multiple models and parallelism configurations, while running 5-17x faster than real GPU execution.