What are the key differences in the pretraining data used for Grok 3 and GPT-4o

1. Training Infrastructure and Compute Power: Grok 3 was trained on xAI's Colossus supercluster, which is one of the largest AI training clusters in the world, utilizing over 100,000 Nvidia H100 GPUs. This provided significantly more compute power compared to previous models, allowing for extensive training on a massive scale[1][3]. In contrast, specific details about GPT-4o's training infrastructure are not as publicly detailed, but it is known that OpenAI also uses large-scale computing resources.

2. Training Data: Grok 3 was trained on a mix of publicly available internet data and proprietary datasets from X (formerly Twitter), providing it with access to real-time data and current events[1][7]. GPT-4o, like other GPT models, is trained on a vast internet dataset but lacks the real-time data integration that Grok 3 benefits from.

3. Context Window and Data Processing: Grok 3 has a context window of up to 1 million tokens, allowing it to process extensive documents and complex prompts efficiently[1]. While GPT-4o also has a large context window, Grok 3's is specifically highlighted for its ability to handle long sequences effectively.

4. Real-Time Data Access: Grok 3 benefits from real-time data access through its integration with X, which enhances its ability to discuss current events and analyze fresh information[5]. GPT-4o does not have this real-time access, as its training data is typically cut off at a certain point in time.