prompter: Large language models need a lot of hardware resources to be trained and also to run inferences. What are the hardware requirements for training GPT-J, OPT and Bloom and what are the requirements to run inference?

assistant: GPT-J and GPT-3 are two large language models, while OPT and Bloom are not specific models, so I am not sure which ones you are referring to. However, I can give you some general information on the hardware requirements for training and inference of large language models.

Training large language models requires a significant amount of computing resources, including CPU, RAM, storage, and specialized hardware such as GPUs or TPUs. GPT-3, for example, was trained on a supercomputer with 175 billion parameters, which required over 3,000 GPUs and consumed over 400,000 core hours of computation. GPT-J is a smaller model than GPT-3, with 6 billion parameters, but still requires a considerable amount of computational resources. The exact hardware requirements for training GPT-J are not known, as it is a community-built model that has been trained on various hardware setups.

Running inference, or using a trained language model to generate text or perform other tasks, also requires substantial computing resources, but generally not as much as training. The specific hardware requirements for inference depend on the size and complexity of the model, as well as the application and the desired response time. Inference can be performed on CPUs or specialized hardware such as GPUs or TPUs, with the latter typically providing better performance for large models. Many cloud providers offer pre-configured virtual machines or containers with hardware optimized for running inference on large language models.

prompter: Is there any language model that isn't so intensive? More specifically is there any language model you are aware of that a normal person could reasonably run on their own hardware, and when I say a normal person's hardware I mean a custom built computer that costs less than $5,000.

assistant: There are three key components needed for machine learning inference: a data source, a machine learning system to process the data, and a data destination. GPUs play an important role in the development of today's machine learning applications. When choosing a GPU for machine learning applications, there are several manufacturers to choose from.  CUDA cores are better for general purpose parallel computing tasks, while Tensor cores are better for deep learning and AI workloads that involve large matrix operations.

prompter: So what kind of equipment are we looking at?

assistant: * GPT-J was trained on a TPU v3-256 for about five weeks
* OPT was trained on 992 NVIDIA 80GB A100 GPUs for about two months, and had to deal with several hardware failures over that time that required either manual or automatic restarts.
* Bloom was trained on the Jean Zay, which had 384 NVIDIA A100 80GB GPUs, and took about four months

The TPUs used by google are not for sale, and a single NVIDIA 80GB A100 GPU can cost 10,000$-20,000$, in addition to the extra components needed to wire everything together and cool everything. In order to train a model on the same time scale as those, if we assumed 300 A100 GPUs was enough, the cost for a facility to train them might be around 6 million$.

prompter: You seem to have missed the question about hardware requirements for inference. Please fill me in.

assistant: 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.

, I appreciate your interest in these fields master. Let me know if i can assist you any further.

prompter: Is there a place that I can rent the hardware needed to run this?