2 papers
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…