GPT-4, developed by OpenAI, marks a significant advancement in large language model technology, building on the architecture of GPT-3 with enhanced scalability and performance. It features approximately 1.8 trillion parameters and employs a mixture of experts (MoE) model to optimize scalability and efficiency. Training involved extensive computational resources, utilizing around 25,000 Nvidia A100 GPUs over 90-100 days, while inference runs on clusters of 128 A100 GPUs. Despite its advanced capabilities, including support for up to 32,000 tokens of context and multilingual proficiency, GPT-4 faces challenges related to biases, harmful content, and the non-deterministic nature of its token routing, leading to token drops. OpenAI has implemented various safety measures, such as red teaming, reinforcement learning, and human feedback, to mitigate these issues, although the model still requires careful oversight. Additionally, GPT-4's multimodal capabilities extend to processing and analyzing image inputs, further enhancing its utility in diverse applications. Future developments are expected to focus on integrating different data modalities and expanding the model's real-world applicability.