3 papers
cs.LG2025
Tensor-Parallelism with Partially Synchronized Activations
Itay Lamprecht, Asaf Karnieli, Yair Hanani +2
Training and inference of Large Language Models (LLMs) with tensor-parallelism requires substantial communication to synchronize activations. Our findings suggest that with a few m…
cs.CV2025
Dual Precision Quantization for Efficient and Accurate Deep Neural Networks Inference
Tomer Gafni, Asaf Karnieli, Yair Hanani
Deep neural networks have achieved state-of-the-art results in a wide range of applications, from natural language processing and computer vision to speech recognition. However, as…
cs.AR2025
Faster Inference of LLMs using FP8 on the Intel Gaudi
Joonhyung Lee, Shmulik Markovich-Golan, Daniel Ohayon +9
Low-precision data types are essential in modern neural networks during both training and inference as they enhance throughput and computational capacity by better exploiting avail…