LLM GPU Memory Calculator

This tool helps you calculate the GPU memory required for serving large language models (LLM) locally. Simply input the number of model parameters (in billions), the quantization number (in bits), and the overhead factor, then click "Calculate" to see the required GPU memory.

For example, for a model Llama3-8B-q4, enter 8 for parameters and 4 for quantization.

Frequently Asked Questions

What do those 8B, 70B mean on large language models?

The "8B" or "70B" in a model's name refers to the number of parameters (weights) in the model:

8B: The model has 8 billion parameters.
70B: The model has 70 billion parameters.

More parameters generally mean the model can capture more complex patterns and relationships, but it also requires more memory and computational power.

What is quantization in large language models?

Quantization is the process of reducing the precision of the numbers used to represent a model's parameters. This involves converting the model's weights from high precision (e.g., 32-bit floating point) to lower precision (e.g., 8-bit or even 4-bit integers). This reduction in precision can significantly decrease the amount of memory needed to store the model and the computational power required for inference.

Why is quantization important?

Quantization is particularly useful for deploying models on resource-constrained devices like mobile phones or embedded systems, where memory and computational power are limited. However, aggressive quantization can sometimes lead to a loss in model accuracy, so it is important to find a balance between efficiency and performance.

What do q4, q8, and f16 represent in a model name?

These notations indicate the level of quantization applied to the model's parameters:

q4: The model's parameters are quantized to 4-bit integers. This is a very aggressive form of quantization, greatly reducing the model's size and memory requirements but potentially impacting accuracy more than higher-bit quantization.
q8: The model's parameters are quantized to 8-bit integers. This is a common quantization level that strikes a balance between reducing memory requirements and maintaining model accuracy.
f16: The model uses 16-bit floating-point precision. This is often used in modern deep learning because it offers a good balance between precision and efficiency.