ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2509.01229
  4. Cited By
LiquidGEMM: Hardware-Efficient W4A8 GEMM Kernel for High-Performance LLM Serving

LiquidGEMM: Hardware-Efficient W4A8 GEMM Kernel for High-Performance LLM Serving

1 September 2025
Huanqi Hu
Bowen Xiao
Shixuan Sun
Jianian Yin
Zhexi Zhang
Xiang Luo
Chengquan Jiang
Weiqi Xu
Xiaoying Jia
Xin Liu
Minyi Guo
    MQVLM
ArXiv (abs)PDFHTMLGithub (748★)

Papers citing "LiquidGEMM: Hardware-Efficient W4A8 GEMM Kernel for High-Performance LLM Serving"

2 / 2 papers shown
Title
CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization
CudaForge: An Agent Framework with Hardware Feedback for CUDA Kernel Optimization
Zijian Zhang
Rong Wang
Shiyang Li
Yuebo Luo
Mingyi Hong
Caiwen Ding
124
0
0
23 Oct 2025
PreScope: Unleashing the Power of Prefetching for Resource-Constrained MoE Inference
PreScope: Unleashing the Power of Prefetching for Resource-Constrained MoE Inference
Enda Yu
Zhaoning Zhang
Dezun Dong
Yongwei Wu
Xiangke Liao
144
1
0
28 Sep 2025
1