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xLLM Technical Report

arXiv – CS AI|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, Yitao Hu, Hui Chen, Tong Yang, Hailong Yang, Jing Li, Guiguang Ding, Ke Zhang|March 4, 2026 at 05:00 AM|1 views

🤖AI Summary

xLLM is a new open-source Large Language Model inference framework that delivers significantly improved performance for enterprise AI deployments. The framework achieves 1.7-2.2x higher throughput compared to existing solutions like MindIE and vLLM-Ascend through novel architectural optimizations including decoupled service-engine design and intelligent scheduling.

Key Takeaways

→xLLM introduces a decoupled service-engine architecture optimized for high-performance enterprise LLM serving across diverse AI accelerators.
→The framework achieves 1.7-2.2x throughput improvements over existing solutions like MindIE and vLLM-Ascend under identical constraints.
→Features include intelligent multimodal request scheduling, dynamic Prefill-Decode disaggregation, and distributed KV Cache management.
→The system incorporates algorithmic enhancements like optimized speculative decoding and comprehensive multi-layer execution pipeline optimizations.
→xLLM framework is publicly available on GitHub for both service and engine components.

#llm #inference #optimization #enterprise-ai #open-source #performance #multimodal #distributed-systems

Read Original →via arXiv – CS AI

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