Quantization can be applied in different contexts, including both LLMs (Large Language Models) and vector databases. While the underlying concept of quantization remains the same, there are some differences in how it is applied and the specific trade-offs involved. Let's explore the differences: Data Representation: LLMs: In LLMs, quantization is primarily applied to reduce the memory requirements of model weights and other parameters. The precision of the floating-point numbers representing the weights is reduced, typically from 32-bit floating-point numbers (FP32) to lower precision formats like 16-bit floating-point numbers (FP16) or 8-bit integers (INT8). Vector Databases: In vector databases, quantization is applied to reduce the memory footprint of high-dimensional vectors. The vectors are typically represented as floating-point numbers, and quantization reduces the precision of these numbers to lower bit representations, such as 8-bit or even lower.
Storing an LLM model and training the same model both require memory, but the memory requirements for training are typically higher than just storing the model. Let's dive into the details: Memory Requirement for Storing the Model: When you store an LLM model, you need to save the weights of the model parameters. Each parameter is typically represented by a 32-bit float (4 bytes). The memory requirement for storing the model weights is calculated by multiplying the number of parameters by 4 bytes. For example, if you have a model with 1 billion parameters, the memory requirement for storing the model weights alone would be 4 GB (4 bytes * 1 billion parameters). Memory Requirement for Training: During the training process, additional components use GPU memory in addition to the model weights. These components include optimizer states, gradients, activations, and temporary variables needed by the training process. These components can require additional memory beyond just storing th