OpenAI launched GPT-5.2, which it describes as its most capable model series so far for professional knowledge work, and the company trained and deployed the model on NVIDIA infrastructure that includes NVIDIA Hopper and GB200 NVL72 systems. The deployment is presented as a showcase of how leading Artificial Intelligence builders are using NVIDIA’s full stack Artificial Intelligence infrastructure to train and serve increasingly advanced models at scale. The collaboration emphasizes the role of specialized accelerators and tightly integrated hardware and software in pushing the capabilities of frontier models.
The article frames GPT-5.2 within a broader trend in Artificial Intelligence, where model capabilities are being advanced through three scaling laws: pretraining, post-training and test-time scaling. It notes that reasoning models, which apply compute during inference to handle complex queries using multiple networks working together, have become widespread. Despite the rise of these inference-heavy approaches, the piece stresses that pre-training and post-training remain the bedrock of intelligence and are central to making reasoning models smarter and more useful.
According to the article, training frontier models from scratch requires very large scale infrastructure, describing how it takes tens of thousands, even hundreds of thousands, of GPUs working together effectively. Achieving this scale demands excellence in multiple dimensions, including world-class accelerators, advanced networking that can handle scale-up, scale-out and increasingly scale-across architectures, and a fully optimized software stack. The article concludes that a purpose-built infrastructure platform designed to deliver performance at scale is essential for enabling the next generation of Artificial Intelligence models such as GPT-5.2.
