How TurboQuant Works for LLMs and Why It Uses Much Less RAM

Most conversations about scaling large language models focus on obvious factors like model size, training data, and GPU power. While those matter, they stop being the main constraint surprisingly quickly. Once you start dealing with long conversations and many users, memory becomes the limiting factor. Not just how much memory you have, but how efficiently you use it. This is especially true during inference, when the model is actively generating responses. At that point, the system is not just running computations, it is also constantly reading and writing large amounts of intermediate data. That data, more than anything else, starts to define both cost and speed. How LLMs actually store words like “cat” When you type a word like “cat,” the model does not store it as text. It converts it into a vector of numbers, often thousands of values long. These numbers represent a position in a high-dimensional space where similar words are located near each other. For example, in a simplified f