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Memory swizzling is the quiet tax that every hierarchical-memory accelerator pays. It is fundamental to how GPUs, TPUs, NPUs, ...

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Explore the significance of model quantization in AI, its methods, and impact on computational efficiency, as detailed by NVIDIA's expert insights. As artificial intelligence (AI) models grow in ...

Abstract: Data-free quantization is known for quantizing a pre-trained deep neural network without access to any training data, which applies to many real-world scenarios in that the training data is ...

This repository contains the code of RoSTE introduced in our work: "RoSTE: An Efficient Quantization-Aware Supervised Fine-Tuning Approach for Large Language Models" To keep computational invariance ...

GGUF quantization implements Post-Training Quantization (PTQ): given an already-trained Llama-like model in high precision, it reduces the bit width of each individual weight. The resulting checkpoint ...