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Calculation of Memory requirement of LLM model of 1 B parameter

 Let's calculate the memory requirement for an LLM model with 1 billion parameters.

  1. Memory Requirement for Model Weights:

    • Each parameter is typically represented by a 32-bit float (4 bytes).
    • To store 1 billion parameters, we multiply 4 bytes by 1 billion, which equals 4 gigabytes (GB) of GPU RAM.
  2. Additional Memory Requirement for Training:
    • During training, there are additional components that use GPU memory, such as optimizer states, gradients, activations, and temporary variables.
    • These components can require approximately 20 extra bytes of memory per model parameter.
    • To account for all these overheads during training, we need to multiply the memory requirement by approximately 6.
    • Therefore, the total memory requirement for training a 1 billion parameter model at 32-bit full precision is approximately 24 GB of GPU RAM.

It's important to note that these calculations are based on the assumption of 32-bit full precision and do not consider any further optimizations or quantization techniques. The memory requirement can vary depending on the specific architecture and implementation of the LLM model.

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