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20232026
most citedHybrid SD: Edge-Cloud Collaborative Inference for Stable Diffusion Models

4 citations · 7 across the 15 of their papers we have counts for

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6 papers · 1 filter

cs.LG2026

HyperDFlash: Hyper-Connection-Aligned Block Speculative Decoding with Gated Residual Reduction

Luxi Lin, Shuang Peng, Rui Ma +7

We present HyperDFlash, a block-parallel speculative decoding framework tailored to DeepSeek-V4's Hyper-Connections (HC). Despite the strong performance of DeepSeek-V4's native Mul…

cs.LG2026

AngelSlim: A more accessible, comprehensive, and efficient toolkit for large model compression

Rui Cen, QiangQiang Hu, Hong Huang +10

This technical report introduces AngelSlim, a comprehensive and versatile toolkit for large model compression developed by the Tencent Hunyuan team. By consolidating cutting-edge a…

cs.LG2026

S2O: Early Stopping for Sparse Attention via Online Permutation

Yu Zhang, Songwei Liu, Chenqian Yan +4

Attention scales quadratically with sequence length, fundamentally limiting long-context inference. Existing block-granularity sparsification can reduce latency, but coarse blocks…

cs.LG2024★ 1 cited

GQSA: Group Quantization and Sparsity for Accelerating Large Language Model Inference

Chao Zeng, Songwei Liu, Shu Yang +3

Model compression has emerged as a mainstream solution to reduce memory usage and computational overhead. This paper presents Group Quantization and Sparse Acceleration (GQSA), a n…

cs.LG2024★ 1 cited

ABQ-LLM: Arbitrary-Bit Quantized Inference Acceleration for Large Language Models

Chao Zeng, Songwei Liu, Yusheng Xie +6

Large Language Models (LLMs) have revolutionized natural language processing tasks. However, their practical application is constrained by substantial memory and computational dema…

cs.LG2024★ 1 cited

FoldGPT: Simple and Effective Large Language Model Compression Scheme

Songwei Liu, Chao Zeng, Lianqiang Li +4

The demand for deploying large language models(LLMs) on mobile devices continues to increase, driven by escalating data security concerns and cloud costs. However, network bandwidt…