How TurboQuant Works for LLMs and Why It Uses...

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