# Meta-Llama-3.1-8B-Instruct Memory Estimation

This repository provides a detailed estimation of GPU memory requirements for generating text using the **Meta-Llama-3.1-8B-Instruct** model. The calculation assumes an input prompt of 4000 tokens and a generated output of 4000 tokens.

## Memory Requirement Formula

To estimate the GPU memory required, the following formula is used:


Total Memory (GB) = (M * P / 10^9) + (T_in * L * H * A * P / 10^9) + (T_out * L * H * A * P / 10^9) + Overhead

Where:

• M: Number of parameters in the model (8 billion for Meta-Llama-3.1-8B-Instruct).
• P: Precision in bytes (2 bytes for FP16).
• L: Number of layers in the model.
• H: Hidden size (number of units in the hidden layer).
• A: Attention heads (number of attention heads in each layer).
• T_in: Input tokens (4000 tokens).
• T_out: Output tokens (4000 tokens).
• Overhead: Includes additional memory required by the framework and any other allocations (e.g., caching, padding, etc.).

Example Calculation

Assume the following values for layers, hidden size, and attention heads:

• L = 32 (number of layers)
• H = 4096 (hidden size)
• A = 32 (number of attention heads)

1. Model Parameters Memory:

Model Memory (GB) = (8 * 10^9 * 2 / 10^9) = 16 GB

2. Intermediate Activations Memory:

Activations Memory (GB) ≈ (4000 * 32 * 4096 * 32 * 2 / 10^9) * 2 ≈ 33 GB

3. Overhead:

Overhead (GB) ≈ 4 GB

Total Memory Estimate:

Total Memory (GB) ≈ 16 GB (Model) + 33 GB (Activations) + 4 GB (Overhead) ≈ 53 GB

Conclusion

For generating 4000 output tokens from a 4000-token input using Meta-Llama-3.1-8B-Instruct at FP16 precision, you would approximately need 53 GB of GPU memory.

A GPU with at least 48 GB to 80 GB of memory (such as an NVIDIA A100 80GB) is recommended for this task.