AI model progress has accelerated tremendously, and in the last six months, models have improved more than in the previous six months. This trend will continue because three scaling laws are stacked together and working in tandem: pre-training scaling, post-training scaling, and inference time scaling. Claude 3.7 shows incredible performance for software engineering. Deepseek v3 shows cost for last generation model capabilities are plummeting in price driving further adoption. OpenAI’s o1 and o3 models showed that longer inference time and search mean much better answers. Like the early days of pre-training laws, there is no limit in sight for adding more compute for post-training these models. This year’s GTC is focused on enabling the explosion in intelligence and tokens. Nvidia is focusing on massive 35x improvements in inference cost to enable the training and deployment of models. Last year’s mantra was “the more you buy, the more you save,” but this year’s slogan is “the more you save, the more you buy.” The inference efficiencies delivered in Nvidia’s roadmaps on the hardware and software side unlock reasoning and agents in the cost-effective deployment of models and other transformational enterprise applications, allowing widespread proliferation and deployment—a classic example of Jevons’ paradox at work. Or how Jensen says it: “the more you buy, the more you make”.

Full commentary : A look at Nvidia’s GTC 2025 announcements, including a focus on addressing pre-training and post-training scaling and inference time scaling working in tandem.