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 have been driving a new wave of interactive AI applications across numerous domains. However%2C efficiently serving LLM inference requests is challenging due to their unpredictable execution times originating from the autoregressive nature of generative models. Existing LLM serving systems exploit first-come-first-serve (FCFS) scheduling%2C suffering from head-of-line blocking issues. To address the non-deterministic nature of LLMs and enable efficient interactive LLM serving%2C we present a speculative shortest-job-first (SSJF) scheduler that uses a light proxy model to predict LLM output sequence lengths. Our open-source SSJF implementation does not require changes to memory management or batching strategies. Evaluations on real-world datasets and production workload traces show that SSJF reduces average job completion times by 30.5-39.6%25 and increases throughput by 2.2-3.6x compared to FCFS schedulers%2C across no batching%2C dynamic batching%2C and continuous batching settings.?quality=80&w=800)
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