7 papers
InstantInfer: Enabling Fast LLM Cold Start with Communicating Finite Automata
Yitao Yuan, Yongchao He, Shaoke Fang +1
Cold starts in large language model (LLM) inference services significantly affect user experience, yet they remain inefficient due to sequential initialization and a massive number…
CascadeInfer: Length-Aware Scheduling of LLM Serving with Low Latency and Load Balancing
Yitao Yuan, Chenqi Zhao, Bohan Zhao +3
Efficiently harnessing GPU compute is critical to improving user experience and reducing operational costs in large language model (LLM) services. However, current inference engine…
A Unified Sparse Attention via Multi-Granularity Compression
Siran Liu, Zane Cao, Yongchao He
Efficient long-context understanding and reasoning are increasingly vital for large language model (LLM) applications such as multi-turn dialogue and program analysis. However, the…
SIMPLE: Disaggregating Sampling from GPU Inference into a Decision Plane for Faster Distributed LLM Serving
Bohan Zhao, Zane Cao, Yongchao He
As large language models (LLMs) scale out with tensor parallelism (TP) and pipeline parallelism (PP) and production stacks have aggressively optimized the data plane (attention/GEM…
HeteroSpec: Leveraging Contextual Heterogeneity for Efficient Speculative Decoding
Siran Liu, Yang Ye, Qianchao Zhu +2
Autoregressive decoding inherently limits the inference throughput of Large Language Model (LLM) due to its sequential dependency. Speculative decoding mitigates this by verifying…
MegatronApp: Efficient and Comprehensive Management on Distributed LLM Training
Bohan Zhao, Guang Yang, Shuo Chen +4
The rapid escalation in the parameter count of large language models (LLMs) has transformed model training from a single-node endeavor into a highly intricate, cross-node activity.…