SmoothQuant: Accurate and Efficient
Post-Training Quantization for Large Language Models
Migrating activation quantization difficulty to weights —  Abstract The paper talks about Large Language Models (LLMs), which are great at their job but require a lot of computing power and memory. Quantization is a method that can make them faster and use less memory, but it doesn’t work well for LLMs with more than 100 billion parameters. The…
SmoothQuant: Accurate and Efficient
Post-Training Quantization for Large Language Models Migrating activation quantization difficulty to weights —  Abstract The paper talks about Large Language Models (LLMs), which are great at their job but require a lot of computing power and memory. Quantization is a method that can make them faster and use less memory, but it doesn’t work well for LLMs with more than 100 billion parameters. The… machine-learning
SmoothQuant Accurate Efficient PostTraining Quantization Large Language Models Migrating activation quantization difficulty weight — Abstract paper talk Large Language Models LLMs great job require lot computing power memory Quantization method make faster use le memory doesn’t work well LLMs 100 billion parameter The… machinelearning