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How Many GPUs Do You Really Need for Model Training?

Large Language Models have revolutionized the field of NLP, enabling applications like language translation, text summarization, question-answering, and more. These models consist of millions or even billions of parameters, which are learned during training using vast amounts of data.

However, running these models requires significant computational resources. In this article, we’ll delve into the computational requirements needed to run LLMs, exploring the factors that influence their complexity and the implications for hardware and software developers.

Are you intrigued by the computational resources necessary for training and running a model? Allow me to delve deeper into the specifics 👇🏼

Regarding Training (Full Training)

Number of GPUs Needed For Training. Image source : Dr. Walid Soula

Where:

• Model’s parameters in billions: The total number of parameters in your model, divided by 1 billion.
• 18 : A factor that accounts for the memory footprint of different components during training (optimizer state (8), gradients (4), weights (6)).
• 1.25: This additional 25% encompasses the…