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.
Llmservingsim2.0: A unified simulator for het- erogeneous hardware and serving techniques in llm infrastructure.IEEE Computer Architecture Letters, 24(2):361–364, July 2025
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KernelSight-LM simulates token-level LLM inference to predict per-kernel latencies and end-to-end metrics (TTFT, TPOT, throughput) with 12.1% and 3.8% kernel errors in cross-generation and target-measured tiers.
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.
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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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KernelSight-LM: A Kernel-Level LLM Inference Simulator
KernelSight-LM simulates token-level LLM inference to predict per-kernel latencies and end-to-end metrics (TTFT, TPOT, throughput) with 12.1% and 3.8% kernel errors in cross-generation and target-measured tiers.
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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.