mlsanityData to 7 Oct 2026

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

arXiv 2601.00397 ↗First posted 1 Jan 2026cs.DC cs.LGcited by 5

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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

  1. 2025Qwen3 Technical Reportcited by 8,195
  2. 2024DistServe: Disaggregating Prefill and Decoding for Goodput-optimized Large Language Model Servingcited by 595
  3. 2023Splitwise: Efficient generative LLM inference using phase splittingcited by 536
  4. 2024Taming Throughput-Latency Tradeoff in LLM Inference with Sarathi-Servecited by 507
  5. 2024Mooncake: A KVCache-centric Disaggregated Architecture for LLM Servingcited by 193
  6. 2023SARATHI: Efficient LLM Inference by Piggybacking Decodes with Chunked Prefillscited by 187
  7. 2025LMCache: An Efficient KV Cache Layer for Enterprise-Scale LLM Inferencecited by 119
  8. 2024Vidur: A Large-Scale Simulation Framework For LLM Inferencecited by 115
  9. 2018Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networkscited by 43
  10. 2024LLMServingSim: A HW/SW Co-Simulation Infrastructure for LLM Inference Serving at Scalecited by 30
  11. 2024Forecasting GPU Performance for Deep Learning Training and Inferencecited by 15
  12. 2024APEX: An Extensible and Dynamism-Aware Simulator for Automated Parallel Execution in LLM Servingcited by 15
3 more
  1. 2025Beyond the Buzz: A Pragmatic Take on Inference Disaggregationcited by 9
  2. 2025TokenSim: Enabling Hardware and Software Exploration for Large Language Model Inference Systemscited by 6
  3. 2025LLMServingSim2.0: A Unified Simulator for Heterogeneous Hardware and Serving Techniques in LLM Infrastructurecited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 5.

5 papers.

Similar, not cited

Close in wording, no citation either way

  1. 2025Boosting LLM Serving through Spatial-Temporal GPU Resource Sharing
  2. 2025DuetServe: Harmonizing Prefill and Decode for LLM Serving via Adaptive GPU Multiplexing
  3. 2026The Serialized Bridge: Understanding and Recovering LLM Serving Performance under Blackwell GPU Confidential Computing
  4. 2024ConServe: Fine-Grained GPU Harvesting for LLM Online and Offline Co-Serving
  5. 2025Predictable LLM Serving on GPU Clusters
  6. 2025Prism: Cost-Efficient Multi-LLM Serving via GPU Memory Ballooning
  7. 2024Saving GPU Hours in LLM Inference System Development and Online Workloads with Simulation and DBMS-Inspired Cache Replacement Policies
  8. 2026FleetOpt: Analytical Fleet Provisioning for LLM Inference with Compress-and-Route as Implementation Mechanism