Reducing the precision of model weights can make deep neural networks run faster in less GPU memory, while preserving model accuracy. If ever there were a salient example of a counter-intuitive ...

TurboQuant vector quantization targets KV cache bloat, aiming to cut LLM memory use by 6x while preserving benchmark accuracy.

Abstract: Quantization has been demonstrated to be one of the most effective model compression solutions that can potentially be adapted to support large models on a resource-constrained edge device ...

Abstract: Quantization is a crucial technique for deploying Large Language Models (LLMs) in resource-constrained environments. However, minimizing performance degradation due to outliers in activation ...

Model quantization bridges the gap between the computational limitations of edge devices and the demands for highly accurate models and real-time intelligent applications. The convergence of ...

Large language models (LLMs) aren’t actually giant computer brains. Instead, they are massive vector spaces in which the probabilities of tokens occurring in a specific order is encoded. Billions of ...