4 citations · 4 across the 5 of their papers we have counts for
4 papers · 1 filter
Communication-free Sampling and 4D Hybrid Parallelism for Scalable Mini-batch GNN Training
Cunyang Wei, Siddharth Singh, Aishwarya Sarkar +7
Graph neural networks (GNNs) are widely used for learning on graph datasets derived from various real-world scenarios. Learning from extremely large graphs requires distributed tra…
Scaling up Test-Time Compute with Latent Reasoning: A Recurrent Depth Approach
Jonas Geiping, Sean McLeish, Neel Jain +6
We study a novel language model architecture that is capable of scaling test-time computation by implicitly reasoning in latent space. Our model works by iterating a recurrent bloc…
Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers
Siddharth Singh, Prajwal Singhania, Aditya Ranjan +9
Training and fine-tuning large language models (LLMs) with hundreds of billions to trillions of parameters requires tens of thousands of GPUs, and a highly scalable software stack.…
Gemstones: A Model Suite for Multi-Faceted Scaling Laws
Sean McLeish, John Kirchenbauer, David Yu Miller +5
Scaling laws are typically fit using a family of models with a narrow range of frozen hyperparameter choices. In this work we study scaling laws using multiple architectural shapes…