FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads
Achieving low latency and high throughput under volatile demand requires deep understanding of real-world serving workloads, yet existing studies often rely on proxy traces or coarse-grained characterizations that fail to capture the heterogeneity of modern multi-model LLM platforms. We present FineServe, an in-the-wild, multi-model LLM serving workload dataset collected from a global commercial marketplace, enabling fine-grained characterization of real-world serving dynamics across heterogeneous models and tasks. Leveraging FineServe, we conduct a comprehensive analysis of arrival dynamics and token behavior, revealing fundamentally different fluctuation regimes across model architectures, scales and task intents.
- ▪Achieving low latency and high throughput under volatile demand requires deep understanding of real-world serving workloads, yet existing studies often rely on proxy traces or coarse-grained characterizations that fail to capture the hetero
- ▪We present FineServe, an in-the-wild, multi-model LLM serving workload dataset collected from a global commercial marketplace, enabling fine-grained characterization of real-world serving dynamics across heterogeneous models and tasks.
- ▪Leveraging FineServe, we conduct a comprehensive analysis of arrival dynamics and token behavior, revealing fundamentally different fluctuation regimes across model architectures, scales and task intents.
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| Original publisher | arXiv.org |
| Canonical URL | https://arxiv.org/abs/2607.19349 |
| Publication time | Thu, 23 Jul 2026 00:00:00 -0400 |
| Retrieval time | 2026-07-23T04:57:25.915Z |
| Last seen | 2026-07-23T08:18:15.641Z |
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Computer Science > Artificial Intelligence arXiv:2607.19349 (cs) [Submitted on 17 Apr 2026] Title:FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads Authors:Tiancheng Zhang, Shaoyuan Huang, Mingyuan Wang, Yunfeng Zhao, Xiaofei Wang, Wenyu Wang View a PDF of the paper titled FineServe: A Fine-Grained Dataset and Characterization of Global LLM Serving Workloads, by Tiancheng Zhang and 5 other authors View PDF HTML (experimental) Abstract:Large language models (LLMs) are increasingly deployed as always-on online services, making efficient LLM serving a critical systems challenge.
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