4 papers
SpecMoE: A Fast and Efficient Mixture-of-Experts Inference via Self-Assisted Speculative Decoding
Jehyeon Bang, Eunyeong Cho, Ranggi Hwang +2
The Mixture-of-Experts (MoE) architecture has emerged as a promising approach to mitigate the rising computational costs of large language models (LLMs) by selectively activating p…
PASCAL: A Phase-Aware Scheduling Algorithm for Serving Reasoning-based Large Language Models
Eunyeong Cho, Jehyeon Bang, Ranggi Hwang +1
The emergence of reasoning-based LLMs leveraging Chain-of-Thought (CoT) inference introduces new serving challenges, as their extended reasoning phases delay user-visible output an…
Debunking the CUDA Myth Towards GPU-based AI Systems
Yunjae Lee, Juntaek Lim, Jehyeon Bang +10
This paper presents a comprehensive evaluation of Intel Gaudi NPUs as an alternative to NVIDIA GPUs, which is currently the de facto standard in AI system design. First, we create…
vTrain: A Simulation Framework for Evaluating Cost-effective and Compute-optimal Large Language Model Training
Jehyeon Bang, Yujeong Choi, Myeongwoo Kim +2
As large language models (LLMs) become widespread in various application domains, a critical challenge the AI community is facing is how to train these large AI models in a cost-ef…