6 papers
SpecLA: Efficient Speculative Decoding for Linear-Attention Models
Zhibin Wang, Xuying Han, Zhaohua Yang +5
Linear-attention models replace the growing KV cache with recurrent states, but autoregressive decoding still reads, updates, and writes these states one token at a time. Speculati…
STAR: Decode-Phase Rescheduling for LLM Inference
Zhibin Wang, Zetao Hong, Xue Li +8
Large Language Model (LLM) inference has emerged as a fundamental paradigm, however, variations in output length cause severe workload imbalance in the decode phase, particularly f…
CoDec: Prefix-Shared Decoding Kernel for LLMs
Zhibin Wang, Rui Ning, Chao Fang +12
Prefix-sharing among multiple prompts presents opportunities to combine the operations of the shared prefix, while attention computation in the decode stage, which becomes a critic…
DART: Diffusion-Inspired Speculative Decoding for Fast LLM Inference
Fuliang Liu, Xue Li, Ketai Zhao +7
Speculative decoding is an effective and lossless approach for accelerating LLM inference. However, existing widely adopted model-based draft designs, such as EAGLE3, improve accur…
Revisiting Service Level Objectives and System Level Metrics in Large Language Model Serving
Zhibin Wang, Shipeng Li, Yuhang Zhou +7
User experience is a critical factor Large Language Model (LLM) serving systems must consider, where service level objectives (SLOs) considering the experience of individual reques…
Echo: Efficient Co-Scheduling of Hybrid Online-Offline Tasks for Large Language Model Serving
Zhibin Wang, Shipeng Li, Xue Li +7
Large language models have been widely deployed in various applications, encompassing both interactive online tasks and batched offline tasks. Given the burstiness and latency sens…