3 papers
cs.AR2026
Scheduling Data to Compute: Hierarchical Data Pipelining for LLM Training on AMD GPUs
Gengyu Rao, Ghadeer Ahmed H Alabandi, Su Ann Chong +28
Large language models (LLM) have been the key focus of recent machine learning breakthroughs. As the model size grows exponentially, the computation needed for training is astronom…
cs.AI2026
ROCm InferaSim: Cut LLM Serving Trial Costs and Deploy the Best Configuration
Anshu Raina, Yao Fu, Zhang Jiejing +11
Serving an LLM efficiently depends on the engine, how the model is split across GPUs, precision, scheduler settings, speculative decoding, KV-cache management, and prefill-decode d…
cs.LG2026
Train to Stitch: Topology-Preserving Regularization for Interoperable Representations
Akshay Malhotra, Nicolas Zilberstein, Shahab Hamidi-Rad
As large, powerful pre-trained models become more prevalent, there is growing interest in developing methods that allow different neural networks to be seamlessly combined with lit…