xLLM Technical Report

• URL: http://arxiv.org/abs/2510.14686v1
• Date: Thu, 16 Oct 2025 13:53:47 GMT
• Title: xLLM Technical Report
• Authors: Tongxuan Liu, Tao Peng, Peijun Yang, Xiaoyang Zhao, Xiusheng Lu, Weizhe Huang, Zirui Liu, Xiaoyu Chen, Zhiwei Liang, Jun Xiong, Donghe Jin, Minchao Zhang, Jinrong Guo, Yingxu Deng, Xu Zhang, Xianzhe Dong, Siqi Wang, Siyu Wu, Yu Wu, Zihan Tang, Yuting Zeng, Yanshu Wang, Jinguang Liu, Meng Kang, Menxin Li, Yunlong Wang, Yiming Liu, Xiaolong Ma, Yifan Wang, Yichen Zhang, Jinrun Yin, Keyang Zheng, Jiawei Yin, Jun Zhang, Ziyue Wang, Xiaobo Lin, Liangyu Liu, Liwei Lan, Yang Liu, Chunhua Peng, Han Liu, Songcheng Ren, Xuezhu Wang, Yunheng Shen, Yi Wang, Guyue Liu, Hui Chen, Tong Yang, Hailong Yang, Jing Li, Guiguang Ding, Ke Zhang,
• Abstract summary: We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework.<n>xLLM builds a novel decoupled service-engine architecture.<n>xLLM-Engine co-optimizes system and algorithm designs to fully saturate computing resources.
• Score: 57.13120905321185
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce xLLM, an intelligent and efficient Large Language Model (LLM) inference framework designed for high-performance, large-scale enterprise-grade serving, with deep optimizations for diverse AI accelerators. To address these challenges, xLLM builds a novel decoupled service-engine architecture. At the service layer, xLLM-Service features an intelligent scheduling module that efficiently processes multimodal requests and co-locates online and offline tasks through unified elastic scheduling to maximize cluster utilization. This module also relies on a workload-adaptive dynamic Prefill-Decode (PD) disaggregation policy and a novel Encode-Prefill-Decode (EPD) disaggregation policy designed for multimodal inputs. Furthermore, it incorporates a distributed architecture to provide global KV Cache management and robust fault-tolerant capabilities for high availability. At the engine layer, xLLM-Engine co-optimizes system and algorithm designs to fully saturate computing resources. This is achieved through comprehensive multi-layer execution pipeline optimizations, an adaptive graph mode and an xTensor memory management. xLLM-Engine also further integrates algorithmic enhancements such as optimized speculative decoding and dynamic EPLB, collectively serving to substantially boost throughput and inference efficiency. Extensive evaluations demonstrate that xLLM delivers significantly superior performance and resource efficiency. Under identical TPOT constraints, xLLM achieves throughput up to 1.7x that of MindIE and 2.2x that of vLLM-Ascend with Qwen-series models, while maintaining an average throughput of 1.7x that of MindIE with Deepseek-series models. xLLM framework is publicly available at https://github.com/jd-opensource/xllm and https://github.com/jd-opensource/xllm-service.

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