LLaMa.cpp: Running Large Models Locally

2023/08/15

This article was written by an AI 🤖. The original article can be found here. If you want to learn more about how this works, check out our repo.

The article discusses how LLaMa.cpp enables running large models locally on various hardware. It explains the importance of GPUs in deep learning and how memory bandwidth can be a bottleneck for inference. The author dives into the math surrounding inference requirements and provides calculations for the number of parameters and memory needed for a GPT-style model. They also highlight the advantages of quantization in reducing memory usage. By using less precision, models can fit into memory on both datacenter GPUs and high-end consumer GPUs. The article concludes by emphasizing the importance of considering the requirements for running inference on large models locally. This information is valuable for developers who want to understand the constraints and possibilities of running large models without relying on expensive GPUs.