While the size of the model parameters gives us a good starting point for estimating memory needs, it's not the whole story. When an LLM processes your input (like a question) and generates an output (like an answer), it performs a vast number of calculations step-by-step through its layers. The intermediate results of these calculations are called activations.

Think of it like solving a complex math problem on a whiteboard. The model parameters are like the learned formulas and constants you have written down permanently. The activations are like the temporary numbers and results you jot down in the working space as you calculate each step towards the final answer. Just as you need space on the whiteboard for these temporary notes, the GPU needs memory (VRAM) to store these activations while it's working.

Why Activations Consume VRAM

Each layer in the neural network takes inputs (either the original input or the activations from the previous layer), processes them using its parameters, and produces new activations as output for the next layer. These activations must be kept in memory until they are no longer needed for subsequent calculations within that specific processing step (often called a "forward pass").

Crucially, the amount of VRAM needed for activations isn't fixed like the model parameters. It's dynamic and depends heavily on the specifics of the task being performed:

Sequence Length (Context Length): Longer input prompts or generating longer responses require the model to keep track of more information. This typically leads to larger activation sizes needing more VRAM. Imagine having to keep more intermediate steps on your whiteboard for a longer problem.
Batch Size: If you process multiple input sequences simultaneously (a "batch") to improve efficiency, the activations for each sequence in the batch need to be stored. Running a batch size of 4 will require roughly four times the activation memory compared to a batch size of 1.
Model Architecture: The internal design of the LLM, such as the number of layers, the "width" of those layers (hidden size), and the attention mechanisms used, directly influences the size and number of activations generated.

Impact on Total VRAM Estimation

So, when estimating the total VRAM required, you need to consider both the static model parameters and the dynamic activations. A more complete (though still simplified) view looks like this:

$Total \, VRAM \approx Memory \, for \, Parameters + Memory \, for \, Activations + Software \, Overhead$

The software overhead accounts for the memory used by the operating system, the GPU driver, and the specific AI framework (like PyTorch or TensorFlow) running the model.

A conceptual breakdown showing that total VRAM usage includes memory for model parameters, activations, and software overhead. The relative sizes are illustrative.

Precisely calculating the activation memory needed beforehand can be tricky, as it depends on runtime factors. However, understanding its existence is very important. The rule of thumb based purely on parameters provides a minimum VRAM estimate. You must always budget for additional space to accommodate activations and overhead, especially if you plan to use long context lengths or process inputs in batches. This explains why a model that theoretically fits based on parameter size might still cause "out-of-memory" errors in practice.