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 the model weights.
- On average, these additional 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 would be approximately 24 GB (4 GB * 6) of GPU RAM.
It's important to note that the memory requirements can vary depending on the specific architecture and implementation of the LLM model, as well as any additional factors such as batch size and the complexity of the training process.
Reducing the memory requirements for training can be crucial, especially for large-scale LLMs, as it allows the models to fit within the memory constraints of GPUs or other hardware accelerators. Techniques like quantization, which reduce the precision of the model weights, can help in reducing the memory requirements for training.
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