arXiv:2508.06133v4 Announce Type: replace-cross Abstract: We study offline scheduling for large language model (LLM) serving under a fixed KV-cache memory budget, where requests have heterogeneous prompt (prefill) and response (decode) lengths. Prompt tokens determine initial KV-cache usage, while each generated token further increases memory consumption, creating dynamic memory constraints during autoregressive decoding. Given a backlog of n requests arriving together, the goal is to form mixed prefill and decode batches over time to minimize total end-to-end latency. We show that heterogeneo
The increasing scale and complexity of Large Language Models demand more efficient serving infrastructure, driving innovation in operational optimization concurrent with rapid LLM development.
Efficient LLM serving reduces operational costs, improves user experience through lower latency, and enables broader deployment of powerful AI models across various applications.
New scheduling algorithms will allow for more dynamic and memory-optimized batching of LLM requests, directly impacting the cost and performance of AI services.
- · Cloud AI providers
- · Companies deploying internal LLMs
- · End-users of AI applications
- · Developers of AI infrastructure software
- · Inefficient LLM serving architectures
- · Companies with high compute overheads
Optimized LLM serving leads to reduced inference costs and faster response times for AI applications.
Lower costs could accelerate the adoption and deployment of more sophisticated AI agents and generative AI features across industries.
The increased efficiency might intensify the demand for high-end AI accelerators, further stressing the compute supply chain and energy grids.
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Read at arXiv cs.LG