Revealing the Secrets of GPT-4: All You Need to Know About OpenAI’s Latest Model
In a world where Artificial Intelligence (AI) has become an integral part of our lives, the recent release of OpenAI’s GPT-4 model has created quite a buzz. Semi-Analysis, a team of AI enthusiasts, has generously provided a wealth of data on GPT-4, shedding light on its architecture, training infrastructure, parameter count, data composition, token count, layer count, multimodal vision adaptation, and more. This information holds immense value, but it comes at a cost. The story takes an intriguing turn as an individual attempted to share the insights with the world, only to encounter copyright issues. However, once something is on the internet, it tends to stay. In this article, we will explore some of the groundbreaking discoveries about GPT-4 and discuss their potential impact on tech giants like Google and Microsoft, as well as the open-source community.
GPT-4 and the Evolution of Model Size
Before we delve into the intriguing details of GPT-4, let’s first understand its size in context. Model size is a crucial metric in AI development as it reflects the model’s capabilities and complexity. To provide some perspective, let’s take a look at the sizes of other language models up until August 2022:
- GPT-3: 175 billion parameters
- Lambda (Google model): 137 billion parameters
- Palmcode or Minerva (Google model): 540 billion parameters
- Ernie (Chinese model): 260 billion parameters
Where does GPT-4 stand in terms of model size? According to the shared data, GPT-4 boasts an impressive 1.8 trillion parameters spread across 128 layers. This makes it roughly ten times larger than its predecessor, GPT-3.5. The exponential growth in model size aligns with the concept of Moore’s Law, but in the realm of AI models, it’s evolving at an astonishing pace, with a 15,000-fold increase in just five years.