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cs.DC2026
LLM-CoOpt: A Co-Design and Optimization Framework for Efficient LLM Inference on Heterogeneous Platforms
Jie Kong, Wei Wang, Jiehan Zhou +1
Major challenges in LLMs inference remain frequent memory bandwidth bottlenecks, computational redundancy, and inefficiencies in long-sequence processing. To address these issues,…
cs.DC2025
Opt4GPTQ: Co-Optimizing Memory and Computation for 4-bit GPTQ Quantized LLM Inference on Heterogeneous Platforms
Yaozheng Zhang, Wei Wang, Jie Kong +5
The increasing adoption of large language models (LLMs) on heterogeneous computing platforms poses significant challenges to achieving high inference efficiency. To address these e…
cs.DC2025
Opt-GPTQ: An Optimized GPTQ Combining Sparse Attention and Quantization Techniques
Jie Kong, Junxiang Zhang, Jiheng Xu +7
In the field of deep learning, traditional attention mechanisms face significant challenges related to high computational complexity and large memory consumption when processing lo…