deployment

Using Quantization to speed up and slim down your LLM

llm, Python, AI, inference optimization, quantization, bitsandbytes, autogptq, autoround, Deep Learning, deployment, llama-factory
Summary Large Language Models (LLMs) are powerful, but their size can lead to slow inference speeds and high memory consumption, hindering real-world deployment. Quantization, a technique that reduces the precision of model weights, offers a powerful solution. This post will explore how to use quantization techniques like bitsandbytes, AutoGPTQ, and AutoRound to dramatically improve LLM inference performance. What is Quantization? Quantization reduces the computational and storage demands of a model by representing its weights with lower-precision data types.