106 citations · 167 across the 26 of their papers we have counts for
10 papers · 1 filter
Taming the Long-Tail: Efficient Reasoning RL Training with Adaptive Drafter
Qinghao Hu, Shang Yang, Junxian Guo +7
The emergence of Large Language Models (LLMs) with strong reasoning capabilities marks a significant milestone, unlocking new frontiers in complex problem-solving. However, trainin…
Semantic-Aware Scheduling for GPU Clusters with Large Language Models
Zerui Wang, Qinghao Hu, Ana Klimovic +4
Deep learning (DL) schedulers are pivotal in optimizing resource allocation in GPU clusters, but operate with a critical limitation: they are largely blind to the semantic context…
Apertus: Democratizing Open and Compliant LLMs for Global Language Environments
Project Apertus, Alejandro Hernández-Cano, Alexander Hägele +100
We present Apertus, a fully open suite of large language models (LLMs) designed to address two systemic shortcomings in today's open model ecosystem: data compliance and multilingu…
Unlocking True Elasticity for the Cloud-Native Era with Dandelion
Tom Kuchler, Pinghe Li, Yazhuo Zhang +8
Elasticity is fundamental to cloud computing, as it enables quickly allocating resources to match the demand of each workload as it arrives, rather than pre-provisioning resources…
Sailor: Automating Distributed Training over Dynamic, Heterogeneous, and Geo-distributed Clusters
Foteini Strati, Zhendong Zhang, George Manos +7
The high GPU demand of ML training makes it hard to allocate large homogeneous clusters of high-end GPUs in a single availability zone. Leveraging heterogeneous GPUs available with…
The Danger of Overthinking: Examining the Reasoning-Action Dilemma in Agentic Tasks
Alejandro Cuadron, Dacheng Li, Wenjie Ma +13
Large Reasoning Models (LRMs) represent a breakthrough in AI problem-solving capabilities, but their effectiveness in interactive environments can be limited. This paper introduces…
Mixtera: A Data Plane for Foundation Model Training
Maximilian Böther, Xiaozhe Yao, Tolga Kerimoglu +3
State-of-the-art large language and vision models are trained over trillions of tokens that are aggregated from a large variety of sources. As training data collections grow, manua…
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…
Understanding GPU Resource Interference One Level Deeper
Paul Elvinger, Foteini Strati, Natalie Enright Jerger +1
GPUs are vastly underutilized, even when running resource-intensive AI applications, as GPU kernels within each job have diverse resource profiles that may saturate some parts of a…