In this web content, the author discusses inference and how to serve a given LLM (large language model) more efficiently. They outline three main strategies: quantization, distillation, and optimization. The author highlights the importance of profiling code to identify overhead and optimize performance. They also emphasize the benefits of distillation over quantization, but note that distillation can be expensive and time-consuming. The author introduces the concept of quantization, explaining how it reduces the precision of weights in a neural network. They discuss the GPTQ method for quantization and its effectiveness in reducing inference accuracy. The author concludes by stating that while quantization can improve performance, it is always a tradeoff with accuracy and may not be worth it in all cases.
https://www.artfintel.com/p/efficient-llm-inference