5 citations · 9 across the 9 of their papers we have counts for
9 papers
ProfInfer: An eBPF-based Fine-Grained LLM Inference Profiler
Bohua Zou, Debayan Roy, Dhimankumar Yogesh Airao +4
As large language models (LLMs) move from research to production, understanding how inference engines behave in real time has become both essential and elusive. Unlike general-purp…
RMLer: Synthesizing Novel Objects across Diverse Categories via Reinforcement Mixing Learning
Jun Li, Zikun Chen, Haibo Chen +2
Novel object synthesis by integrating distinct textual concepts from diverse categories remains a significant challenge in Text-to-Image (T2I) generation. Existing methods often su…
FairBatching: Fairness-Aware Batch Formation for LLM Inference
Hongtao Lyu, Boyue Liu, Mingyu Wu +1
Large language model (LLM) inference systems face a fundamental tension between minimizing Time-to-First-Token (TTFT) latency for new requests and maintaining a high, steady token…
SmallThinker: A Family of Efficient Large Language Models Natively Trained for Local Deployment
Yixin Song, Zhenliang Xue, Dongliang Wei +11
While frontier large language models (LLMs) continue to push capability boundaries, their deployment remains confined to GPU-powered cloud infrastructure. We challenge this paradig…
DIP: Efficient Large Multimodal Model Training with Dynamic Interleaved Pipeline
Zhenliang Xue, Hanpeng Hu, Xing Chen +7
Large multimodal models (LMMs) have demonstrated excellent capabilities in both understanding and generation tasks with various modalities. While these models can accept flexible c…
PipeLLM: Fast and Confidential Large Language Model Services with Speculative Pipelined Encryption
Yifan Tan, Cheng Tan, Zeyu Mi +1
Confidential computing on GPUs, like NVIDIA H100, mitigates the security risks of outsourced Large Language Models (LLMs) by implementing strong isolation and data encryption. None…
Turbo Sparse: Achieving LLM SOTA Performance with Minimal Activated Parameters
Yixin Song, Haotong Xie, Zhengyan Zhang +4
Exploiting activation sparsity is a promising approach to significantly accelerating the inference process of large language models (LLMs) without compromising performance. However…
PowerInfer-2: Fast Large Language Model Inference on a Smartphone
Zhenliang Xue, Yixin Song, Zeyu Mi +3
Large language models (LLMs) on smartphones enable real-time AI assistance and privacy-preserving, offline operation. However, resource constraints of smartphones limit current dep…
PowerInfer: Fast Large Language Model Serving with a Consumer-grade GPU
Yixin Song, Zeyu Mi, Haotong Xie +1
This paper introduces PowerInfer, a high-speed Large Language Model (LLM) inference engine on a personal computer (PC) equipped with a single consumer-grade GPU. The key principle…