Training GPT-J, OPT, and Bloom require high-performance hardware to run efficiently.
 This is possibly some general hardware requirements for training and running inference for these models:
 I have included a more directly correlated list appended to the end of this text:

GPT-J:
GPT-J is one of the largest and most powerful language models to date. Training this model requires massive amounts of computational power and resources. The hardware requirements for training GPT-J include:

Multiple GPUs with high memory capacity (e.g., 16-32GB)
High-performance CPUs with multiple cores and high clock speeds
Large amounts of RAM (e.g., 256GB-1TB)
High-speed storage (e.g., NVMe SSDs)
Running inference for GPT-J requires less powerful hardware than training. The hardware requirements for running inference include:

A high-performance CPU with multiple cores and high clock speeds
Sufficient RAM to load the model and handle the output
Fast storage for loading and saving the model and data
OPT:
OPT is a smaller language model than GPT-J, but it still requires significant hardware resources to train. The hardware requirements for training OPT include:

Multiple GPUs with high memory capacity (e.g., 8-16GB)
High-performance CPUs with multiple cores and high clock speeds
Large amounts of RAM (e.g., 64GB-256GB)
High-speed storage (e.g., NVMe SSDs)
Running inference for OPT requires less powerful hardware than training. The hardware requirements for running inference include:

A high-performance CPU with multiple cores and high clock speeds
Sufficient RAM to load the model and handle the output
Fast storage for loading and saving the model and data
Bloom:
Bloom is a graph-based machine learning model that requires specialized hardware resources to train and run. The hardware requirements for training Bloom include:

Graph processing units (GPUs) or tensor processing units (TPUs) that are optimized for graph-based computations
High-performance CPUs with multiple cores and high clock speeds
Large amounts of RAM (e.g., 64GB-256GB)
High-speed storage (e.g., NVMe SSDs)
Running inference for Bloom also requires specialized hardware resources. The hardware requirements for running inference include:

A GPU or TPU that is optimized for graph-based computations
Sufficient RAM to load the model and handle the output
Fast storage for loading and saving the model and data

Here is a more comprehensive list if you prefer:

GPT-J: Requires powerful computing resources such as Nvidia GPUs and high-performance CPUs. Specifically, OpenAI's GPT-J-6B, which has 6 billion parameters, requires at least 1,536 Nvidia V100 GPUs and 400 GB of CPU memory. Running inference on GPT-J-6B requires a single high-end GPU.

GPT-3: Requires even more powerful computing resources than GPT-J, with models ranging from 175 billion to 13 trillion parameters. For example, the GPT-3 model with 175 billion parameters requires 3,200 Nvidia V100 GPUs and 1.5 TB of CPU memory for training. Running inference on GPT-3 requires a powerful GPU or a cluster of CPUs.

Bloom: The hardware requirements for training and inference of Bloom depend on the size of the dataset and the complexity of the model. For example, training Bloom on a dataset of 100 million items might require a high-performance GPU, while training on a dataset of 1 billion items might require a distributed computing system with multiple GPUs or CPUs. Running inference on Bloom requires a GPU or a CPU, depending on the size of the model.

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