arXiv:2512.09472v2 Announce Type: replace-cross Abstract: Deploying multiple models within shared GPU clusters is a key strategy to improve resource efficiency in large language model (LLM) serving. Existing multi-LLM serving systems improve GPU utilization at the cost of degraded inference performance, particularly time-to-first-token (TTFT). We attribute this degradation to the lack of awareness regarding future workload characteristics. In contrast, recent analyses have shown the strong periodicity and long-term predictability of real-world LLM serving workloads. In this paper, we propose o
The paper addresses an immediate challenge in LLM serving as the demand for diverse model deployments within shared GPU infrastructure increases, prompting the development of more efficient resource management strategies.
Improved GPU utilization and reduced time-to-first-token in multi-LLM serving directly impact the cost-efficiency and responsiveness of AI applications, making large language models more economically viable and performant.
The proposed 'WarmServe' system enables more efficient sharing of GPU resources for multiple LLMs by leveraging workload predictability, potentially leading to lower operational costs and better user experiences for AI services.
- · AI service providers
- · Cloud infrastructure providers
- · Developers building multi-LLM applications
- · Organizations seeking cost-efficient LLM deployment
- · Inefficient GPU-bound AI services
- · Companies with monolithic LLM deployment strategies
Reduced operational costs for large language model inference.
Accelerated adoption and commercialization of AI applications reliant on multiple LLMs.
Increased competition among AI service providers due to lower barriers to entry for model hosting.
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