Abstract: Machine learning (ML) models have been rapidly growing in size to improve accuracy, but this has greatly increased their computational cost. Leveraging the unstructured sparsity in the ...

Sparse Autoencoders (SAEs) have recently been shown to enhance interpretability and steerability in Large Language Models (LLMs). In this work, we extend the application of SAEs to Vision-Language ...

Abstract: Sparse Matrix-Matrix Multiplication (SpMM) is a fundamental operation in graph computing and analytics. However, the irregularity of real-world graphs poses significant challenges to ...

On a B200, the nvjet_tst_16x64_64x16_4x1_v_bz_TNN kernel is used, and it takes roughly 8.1 microseconds. On a H200, the nvjet_tst_64x8_64x16_4x1_v_bz_TNT kernel is ...