ChatGPT run on servers hosted by OpenAI. The servers that run the model are equipped with powerful GPUs (Graphics Processing Units) that allow for fast and efficient training and inference.
The specific type and number of GPUs used can vary depending on the resources available and the specific implementation of the model. For example, the GPT-3 model, which is one of the most advanced language models currently available, is trained on several powerful GPUs to speed up the process.
It’s important to note that the speed of the model’s inference, that is the time it takes to generate a response, depends on the complexity of the task and the amount of data the model has been trained on. However, in general, large language models like GPT-3 are able to generate responses in a matter of milliseconds.
Additionally, to speed up the process, the model could be run on distributed computing clusters, which allows to split the workload across several machines, thus reducing the time required to complete the task.
Language models like ChatGPT are typically trained and run on GPUs (Graphics Processing Units) rather than CPUs (Central Processing Units) for several reasons:
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Speed: GPUs are designed to perform large numbers of calculations in parallel, making them well-suited for the complex mathematical operations required to train a neural network. In contrast, CPUs are optimized for sequential processing and are not as well-suited for parallel processing tasks. This means that training a large neural network on a CPU can take much longer than training it on a GPU.
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Memory: Large language models like ChatGPT require a significant amount of memory to store the model parameters and intermediate values during training. GPUs have more memory than CPUs, which allows them to handle large models more efficiently.
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Power: Training a large neural network on a CPU requires a significant amount of power, which can be costly and inefficient. In contrast, GPUs are more energy-efficient than CPUs and can perform the same workload using less power.
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Cost: Training a large neural network on a CPU can be more expensive, as it requires a significant amount of computational resources and energy. In contrast, the cost of training on a GPU has been decreasing in recent years, making it more affordable for researchers and developers.
It’s worth noting that, even though GPUs are commonly used to train language models, it’s also possible to use TPUs (Tensor Processing Units) which are specialized hardware developed by Google specifically for machine learning workloads and they provide even more computational power than GPUs.