5 papers
Not All Prefills Are Equal: PPD Disaggregation for Multi-turn LLM Serving
Zongze Li, Jingyu Liu, Zhen Xu +3
Prefill-Decode (PD) disaggregation has become the standard architecture for modern LLM inference engines, which alleviates the interference of two distinctive workloads. With the g…
Evolving Contextual Safety in Multi-Modal Large Language Models via Inference-Time Self-Reflective Memory
Ce Zhang, Jinxi He, Junyi He +2
Multi-modal Large Language Models (MLLMs) have achieved remarkable performance across a wide range of visual reasoning tasks, yet their vulnerability to safety risks remains a pres…
TiDAR: Think in Diffusion, Talk in Autoregression
Jingyu Liu, Xin Dong, Zhifan Ye +6
Diffusion language models hold the promise of fast parallel generation, while autoregressive (AR) models typically excel in quality due to their causal structure aligning naturally…
HAMburger: Accelerating LLM Inference via Token Smashing
Jingyu Liu, Ce Zhang
The growing demand for efficient Large Language Model (LLM) inference requires a holistic optimization on algorithms, systems, and hardware. However, very few works have fundamenta…
Speculative Prefill: Turbocharging TTFT with Lightweight and Training-Free Token Importance Estimation
Jingyu Liu, Beidi Chen, Ce Zhang
Improving time-to-first-token (TTFT) is an essentially important objective in modern large language model (LLM) inference engines. Optimizing TTFT directly results in higher maxima…