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cs.AR2026
Dissecting How Die Scaling Breaks GPU Fine-grained Scheduling
Xiaoze Fan, Jianhao Wang, Weihao Cui +8
Modern GPUs are no longer physically symmetric. Die scaling leads to both manufacturing-driven floorsweeping and cache and memory partitioning. The former creates chip-specific com…
cs.AR2026
Approaching Shannon Bound with Lossless LLM Weight Compression
Hongshi Tan, Yao Chen, Gustavo Alonso +2
Large language models (LLMs) now scale to trillions of parameters, driving weight storage into the terabyte regime and creating an acute mismatch with GPU memory capacity. Although…
cs.DC2026
MuxTune: Efficient Multi-Task LLM Fine-Tuning in Multi-Tenant Datacenters via Spatial-Temporal Backbone Multiplexing
Chunyu Xue, Yi Pan, Weihao Cui +4
Parameter-Efficient Fine-Tuning (PEFT) is widely applied as the backend of fine-tuning APIs for large language model (LLM) customization in datacenters. Service providers deploy se…