GPT-5 Model Architecture

GPT-5 Model Architecture

Here’s how OpenAI developed GPT-5. Let’s delve into the recent discussions surrounding GPT-5’s architecture. This information remains speculative, deriving from various articles and tweet threads.

GPT-5 Model

Firstly, the size of GPT-5 is noteworthy. It’s approximately 10 times larger than its predecessor, GPT-3, boasting around 1.8 trillion parameters across 120 layers of deep neural networks. In the realm of deep learning, the number of parameters significantly influences a model’s capabilities.

What’s particularly fascinating is the conditional computational method utilized, known as Mixture of Experts (MoE). We’ll refer to it as Mo. MoE is a neural network architecture that employs conditional computation to boost model capacity without engaging the entire network for every task. This method segments the network into various sections or “experts,” each activated by specific input examples.

To put it in perspective, imagine managing a call center where different types of issues are directed to specialized agents. Similarly, with Mo, inputs are routed to the specific expert best suited to address them. This approach allows the power of multiple specialized models to be harnessed collectively. It enabling the system to scale its capacity without a proportional increase in computational demand.


In GPT-5’s case, OpenAI integrated 16 experts, each comprising approximately 111 billion parameters. Therefore, for the generation of a single token (inference), the model only employs 280 billion parameters, a significantly reduced requirement compared to a purely dense model. This efficiency is Mo’s primary advantage, as it maintains cost-effectiveness.

Another critical aspect highlighted is GPT-5’s training on 13 trillion tokens, undergoing two epochs for text data and four for code-based data. The model also benefited from instruction fine-tuning data provided by Scale AI.

A noteworthy point is the estimated cost of training a model equivalent to GPT-5 in the cloud, which would be around $63 million. These insights underscore the potential of the Mixture of Experts technique in revolutionizing large-scale model building, offering a more efficient scaling method while reducing computational expenses.


The adoption of Mo by OpenAI in GPT-5 could inspire more AI developers to explore this technique for their projects. Its spotlight might significantly influence the development strategies for large language models, potentially making Mo a popular method in the AI community.

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