arXiv:2604.00499v2 Announce Type: replace Abstract: To schedule LLM inference, the \textit{shortest job first} (SJF) principle is favorable by prioritizing requests with short output lengths to avoid head-of-line (HOL) blocking. Existing methods usually predict a single output length for each request to facilitate scheduling. We argue that such a \textit{point estimate} does not match the \textit{stochastic} decoding process of LLM inference, where output length is \textit{uncertain} by nature and determined by when the end-of-sequence (EOS) token is sampled. Hence, the output length of each r
The rapid development and widespread adoption of Large Language Models (LLMs) are driving research into optimizing their performance and resource utilization, making scheduling challenging.
Efficient scheduling of LLM inference directly impacts the cost, latency, and scalability of AI applications, which is critical for their commercial deployment and widespread adoption.
This research proposes a more sophisticated approach to LLM inference scheduling by accounting for the inherent uncertainty in output length, potentially leading to more efficient resource allocation compared to current point estimate methods.
- · Cloud providers
- · AI infrastructure companies
- · Developers of LLM applications
- · Data centers
- · Companies with inefficient LLM scheduling algorithms
- · Users experiencing high LLM inference latency
Improved resource utilization and reduced operational costs for LLM inference.
Faster and more reliable AI services, accelerating adoption in various industries.
Increased demand for specialized hardware and software optimized for stochastic AI workloads.
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Read at arXiv cs.LG