3 papers
cs.LG2026
To 2:4 Sparsity and Beyond: Neuron-level Activation Function to Accelerate LLM Pre-Training
Meghana Madhyastha, Daniel Haziza, Jesse Cai +3
Trainings of Large Language Models are generally bottlenecked by matrix multiplications. In the Transformer architecture, a large portion of these operations happens in the Feed Fo…
cs.LG2025
TorchAO: PyTorch-Native Training-to-Serving Model Optimization
Andrew Or, Apurva Jain, Daniel Vega-Myhre +10
We present TorchAO, a PyTorch-native model optimization framework leveraging quantization and sparsity to provide an end-to-end, training-to-serving workflow for AI models. TorchAO…
cs.LG2025
Accelerating Transformer Inference and Training with 2:4 Activation Sparsity
Daniel Haziza, Timothy Chou, Dhruv Choudhary +7
In this paper, we demonstrate how to leverage 2:4 sparsity, a popular hardware-accelerated GPU sparsity pattern, to activations to accelerate large language model training and infe…