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MetaTimer: Using Large Language Models for Precise, Prompt-Aware Inference Latency Prediction The rapid proliferation of large language models (LLMs) in production systems has exposed a fundamental limitation: inference latency varies dramatically across prompts due to differences in semantic complexity, required reasoning depth, output length, and generation dynamics. Conventional prediction methods—ranging from token-count heuristics and hardware Roofline models to traditional machine-learning regressors—fail to generalize because they cannot capture these prompt-specific nuances. Accurate a priori estimation of processing time is essential for resource scheduling, dynamic batching, cost forecasting, service-level guarantees, and user-experience enhancements. We introduce MetaTimer, the first framework to repurpose a lightweight LLM itself as a high-precision meta-predictor capable of forecasting the exact wall-clock inference duration required by any target LLM for an arbitrary input prompt. A compact 8B-parameter model is fine-tuned on a massive corpus of millions of prompt–execution pairs collected across heterogeneous model families (GPT-4-class, Llama 3.1, Claude, Mistral), quantization levels, decoding strategies, and hardware accelerators. The predictor employs chain-of-thought reasoning to decompose prompt semantics, estimate output token distributions and reasoning trajectories, and integrate model- and hardware-specific performance profiles, yielding fine-grained predictions for Time-to-First-Token (TTFT), Time-Per-Output-Token (TPOT), and total latency. Extensive evaluations on held-out benchmarks spanning reasoning, creative writing, coding, and long-context tasks demonstrate state-of-the-art accuracy: a mean absolute percentage error (MAPE) of 6.3% for end-to-end latency—representing a >40% reduction in mean squared error relative to the strongest Roofline–ML baselines—and strong zero-shot generalization to unseen models and platforms. When integrated into production serving stacks (vLLM, TensorRT-LLM, Triton), MetaTimer delivers up to 31% gains in resource utilization and tail-latency reduction. These results establish that LLMs possess emergent capabilities for computational self-modeling, opening a new paradigm for self-aware, adaptive, and energy-efficient generative AI infrastructure. We publicly release the predictor model, dataset, and serving plugins to accelerate research in meta-performance modeling for frontier AI systems.