Revati: Transparent GPU-Free Time-Warp Emulation for LLM Serving
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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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 15 of its 30 references that are in the map. First posted on arXiv 1 Jan 2026.
Builds on
15 of 30 references.
References · 15
- 2025Qwen3 Technical Reportcited by 8,195
- 2024DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Servingcited by 595
- 2023Splitwise: Efficient generative LLM inference using phase splittingcited by 536
- 2024Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Servecited by 507
- 2024Mooncake: A KVCache-centric Disaggregated Architecture for LLM Servingcited by 193
- 2023SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefillscited by 187
- 2025LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inferencecited by 119
- 2024Vidur: A Large-Scale Simulation Framework For LLM Inferencecited by 115
- 2018Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networkscited by 43
- 2024LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scalecited by 30
- 2024Forecasting GPU Performance for Deep Learning Training and Inferencecited by 15
- 2024APEX: An Extensible and Dynamism-Aware Simulator for Automated Parallel Execution in LLM Servingcited by 15
3 more
- 2025Beyond the Buzz: A Pragmatic Take on Inference Disaggregationcited by 9
- 2025TokenSim: Enabling Hardware and Software Exploration for Large Language Model Inference Systemscited by 6
- 2025LLMServingSim2.0: A Unified Simulator for Heterogeneous Hardware and Serving Techniques in LLM Infrastructurecited by 6
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Citing papers per year: 2026: 5.
5 papers.
- 2026Frontier: Towards Comprehensive and Accurate LLM Inference Simulationcited by 4
- 2026Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulationcited by 3
- 2026AgentServeSim: Serving-System Simulation and Policy Search for LLM Agent Programscited by 1
- 2026LLM-Emu: Native Runtime Emulation of LLM Inference via Profile-Driven Samplingcited by 0
- 2026Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulationcited by 0
- 2026AgentServeSim: Serving-System Simulation and Policy Search for LLM Agent Programs8 Jun 2026
- 2026Characterization of Multi-Model Agentic AI Systems on General Tasks via Trace-Driven Simulation1 Jun 2026
- 2026Frontier: Towards Comprehensive and Accurate LLM Inference Simulation20 May 2026
- 2026Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation8 May 2026
- 2026LLM-Emu: Native Runtime Emulation of LLM Inference via Profile-Driven Sampling1 May 2026
Similar, not cited
Close in wording, no citation either way
- 2025Boosting LLM Serving through Spatial-Temporal GPU Resource Sharing
- 2025DuetServe: Harmonizing Prefill and Decode for LLM Serving via Adaptive GPU Multiplexing
- 2026The Serialized Bridge: Understanding and Recovering LLM Serving Performance under Blackwell GPU Confidential Computing
- 2024ConServe: Fine-Grained GPU Harvesting for LLM Online and Offline Co-Serving
- 2025Predictable LLM Serving on GPU Clusters
- 2025Prism: Cost-Efficient Multi-LLM Serving via GPU Memory Ballooning
- 2024Saving GPU Hours in LLM Inference System Development and Online Workloads with Simulation and DBMS-Inspired Cache Replacement Policies
- 2026FleetOpt: Analytical Fleet Provisioning for LLM Inference with Compress-and-Route as Implementation Mechanism