Frontier is a new discrete-event simulator for disaggregated LLM serving that incorporates co-location, PDD, AFD, and optimizations, achieving under 4% throughput error and large reductions in latency prediction error versus prior simulators.
Revati: Transparent gpu-free time-warp emulation for llm serving
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LLM-Emu is a serving-native emulator for vLLM that replaces GPU execution with profile-driven latency sampling and achieves under 5% error on TPOT, ITL, E2E latency, and throughput across multiple models, GPUs, and workloads.
Dooly reduces LLM inference profiling GPU-hours by 56.4% across 12 models while keeping simulation MAPE under 5% for TTFT and 8% for TPOT by making profiling configuration-agnostic and redundancy-aware.
citing papers explorer
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Frontier: Towards Comprehensive and Accurate LLM Inference Simulation
Frontier is a new discrete-event simulator for disaggregated LLM serving that incorporates co-location, PDD, AFD, and optimizations, achieving under 4% throughput error and large reductions in latency prediction error versus prior simulators.
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LLM-Emu: Native Runtime Emulation of LLM Inference via Profile-Driven Sampling
LLM-Emu is a serving-native emulator for vLLM that replaces GPU execution with profile-driven latency sampling and achieves under 5% error on TPOT, ITL, E2E latency, and throughput across multiple models, GPUs, and workloads.
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Dooly: Configuration-Agnostic, Redundancy-Aware Profiling for LLM Inference Simulation
Dooly reduces LLM inference profiling GPU-hours by 56.4% across 12 models while keeping simulation MAPE under 5% for TTFT and 8% for TPOT by making profiling configuration-agnostic and redundancy-aware.