57 citations · 75 across the 26 of their papers we have counts for
30 papers
AFORE: Attention-FFN Disaggregation with Overlapped Reconfiguration of Experts
Wenshuang Li, Youhe Jiang, You Peng +2
Efficient serving of Mixture-of-Experts (MoE) models is challenging due to large expert parameters, input-dependent expert activation, and dynamic workloads. Expert parallelism dis…
Coda: Exploiting Admission Flexibility for Coding-Agent Serving
Youhe Jiang, Fangcheng Fu, Binhang Yuan +5
Coding agents powered by large language models (LLMs) repeatedly alternate between model inference and tool calls, creating long-lived sessions with reusable key-value (KV) states…
Vidu S1: A Real-Time Interactive Video Generation Model
Jintao Zhang, Kai Jiang, Jintao Chen +24
We introduce Vidu S1, a real-time interactive video generation model supporting voice control of digital characters. Users can control video generation content at any moment throug…
Next-Generation Agentic Reinforcement Learning Systems Enable Self-Evolving Agents
Ran Yan, Wei Fu, Jiale Li +21
LLM agents are rapidly being deployed in production, including coding assistants, customer-support chatbots, and scientific research assistants, yet they remain fundamentally stati…
TurboServe: Serving Streaming Video Generation Efficiently and Economically
Youhe Jiang, Haoxu Wang, Haotong Bao +5
Streaming video generation is emerging as a new serving workload in which users interact with long-lived sessions that generate video progressively, chunk by chunk. Unlike offline…
HexAGenT: Efficient Agentic LLM Serving via Workflow- and Heterogeneity-Aware Scheduling
You Peng, Youhe Jiang, Wenshuang Li +5
Agentic LLM applications increasingly execute user requests as multi-step workflows involving planning, tool use, branching, refinement, and synthesis. In such settings, users expe…
HexiSeq: Accommodating Long Context Training of LLMs over Heterogeneous Hardware
Yan Liang, Youhe Jiang, Ran Yan +3
Long-context training of large language models (LLMs) is commonly distributed with Context Parallelism (CP) and Head Parallelism (HP), but existing training systems largely assume…
Autopoiesis: A Self-Evolving System Paradigm for LLM Serving Under Runtime Dynamics
Youhe Jiang, Ran Yan, You Peng +4
Modern Large Language Model (LLM) serving operates in highly volatile environments characterized by severe runtime dynamics, such as workload fluctuations and elastic cluster autos…
SLA2: Sparse-Linear Attention with Learnable Routing and Quantization-aware Training
Jintao Zhang, Haoxu Wang, Kai Jiang +6
Sparse-Linear Attention (SLA) combines sparse and linear attention to accelerate diffusion models and has shown strong performance in video generation. However, (i) SLA relies on a…
BOute: Cost-Efficient LLM Serving with Heterogeneous LLMs and GPUs via Multi-Objective Bayesian Optimization
Youhe Jiang, Fangcheng Fu, Eiko Yoneki
The rapid growth of large language model (LLM) deployments has made cost-efficient serving systems essential. Recent efforts to enhance system cost-efficiency adopt two main perspe…
Efficient Multi-round LLM Inference over Disaggregated Serving
Wenhao He, Youhe Jiang, Penghao Zhao +4
With the rapid evolution of Large Language Models (LLMs), multi-round workflows, such as autonomous agents and iterative retrieval, have become increasingly prevalent. However, thi…
OServe: Accelerating LLM Serving via Spatial-Temporal Workload Orchestration
Youhe Jiang, Fangcheng Fu, Taiyi Wang +2
Serving Large Language Models (LLMs) can benefit immensely from parallelizing both the model and input requests across multiple devices, but incoming workloads exhibit substantial…
AReaL-Hex: Accommodating Asynchronous RL Training over Heterogeneous GPUs
Ran Yan, Youhe Jiang, Tianyuan Wu +7
Maximizing training throughput and cost-efficiency of RL for LLMs is essential to democratize this advanced technique. One promising but challenging approach is to deploy such a co…
Parallax: Efficient LLM Inference Service over Decentralized Environment
Chris Tong, Youhe Jiang, Gufeng Chen +6
Deploying a large language model (LLM) inference service remains costly because centralized serving depends on specialized GPU clusters and high-bandwidth interconnects in datacent…
Efficient Pre-Training of LLMs via Topology-Aware Communication Alignment on More Than 9600 GPUs
Guoliang He, Youhe Jiang, Wencong Xiao +8
The scaling law for large language models (LLMs) depicts that the path towards machine intelligence necessitates training at large scale. Thus, companies continuously build large-s…
FSA: An Alternative Efficient Implementation of Native Sparse Attention Kernel
Ran Yan, Youhe Jiang, Zhuoming Chen +3
Recent advances in sparse attention mechanisms have demonstrated strong potential for reducing the computational cost of long-context training and inference in large language model…
LMDeploy Accelerates Mixed-Precision LLM Inference with TurboMind
Li Zhang, Youhe Jiang, Guoliang He +6
Mixed-precision inference techniques reduce the memory and computational demands of Large Language Models (LLMs) by applying hybrid precision formats to model weights, activations,…
Cascadia: An Efficient Cascade Serving System for Large Language Models
Youhe Jiang, Fangcheng Fu, Wanru Zhao +4
Recent advances in large language models (LLMs) have intensified the need to deliver both rapid responses and high-quality outputs. More powerful models yield better results but in…
Thinking Short and Right Over Thinking Long: Serving LLM Reasoning Efficiently and Accurately
Yuhang Wang, Youhe Jiang, Bin Cui +1
Recent advances in test-time scaling suggest that Large Language Models (LLMs) can gain better capabilities by generating Chain-of-Thought reasoning (analogous to human thinking) t…
HEXGEN-FLOW: Optimizing LLM Inference Request Scheduling for Agentic Text-to-SQL
You Peng, Youhe Jiang, Wenqi Jiang +2
Recent advances in agentic large language models (LLMs) have substantially improved Text-to-SQL, enabling users without database expertise to query databases intuitively. However,…
HexGen-2: Disaggregated Generative Inference of LLMs in Heterogeneous Environment
Youhe Jiang, Ran Yan, Binhang Yuan
Disaggregating the prefill and decoding phases represents an effective new paradigm for generative inference of large language models (LLM), which eliminates prefill-decoding inter…
ThunderServe: High-performance and Cost-efficient LLM Serving in Cloud Environments
Youhe Jiang, Fangcheng Fu, Xiaozhe Yao +4
Recent developments in large language models (LLMs) have demonstrated their remarkable proficiency in a range of tasks. Compared to in-house homogeneous GPU clusters, deploying LLM…
Demystifying Cost-Efficiency in LLM Serving over Heterogeneous GPUs
Youhe Jiang, Fangcheng Fu, Xiaozhe Yao +6
Recent advancements in Large Language Models (LLMs) have led to increasingly diverse requests, accompanied with varying resource (compute and memory) demands to serve them. However…
Revisiting the Time Cost Model of AllReduce
Dian Xiong, Li Chen, Youhe Jiang +3
AllReduce is an important and popular collective communication primitive, which has been widely used in areas such as distributed machine learning and high performance computing. T…