Timothy Morano
February 13, 2025 19:38
See how the AI scaling laws, including pre -adjustment, training and testing time scaling, enhance the performance and intelligence of the AI model to lead the demand for accelerated computing.
According to a recent NVIDIA blog post, the AI scaling law has revolutionized the way the artificial intelligence model is developed and optimized. This law explains how to improve model performance by increasing the size of educational data, model parameters and computational resources.
Preliminary scaling understanding
Preliminary scaling is the cornerstone of AI development. By expanding educational data sets, model parameters, and calculation resources, developers can achieve predictable improvements in model accuracy and intelligence. This scaling principle has created a large model with groundbreaking functions such as mixing of 1 billion and 100 million parameter transformers and expert models.
Scaling technology after training
If the basic model is in advance, you can adapt to a specific application through post -training scaling. This process includes technologies such as fine adjustment, pruning and distillation to improve the specificity and relevance of the model. After training, scaling may require much more computing resources than preliminary adjustments, and can lead the demand for accelerated computing throughout the industry.
Test time scaling role
Test time scaling or long thinking is a technology that applies additional calculation efforts in the reasoning stage to improve the AI reasoning function. This allows the model to infer through various solutions to solve complex multi -level problems. Test time scaling is important for tasks that require detailed reasoning, such as medical and logistics.
In the medical sector, testing time scaling can help you analyze a large data set to predict disease progression and potential treatment complications. Logistics can help complex decisions and improve the improvement of demand forecast and supply chain management.
The rise in AI reasoning models, such as O1-Mini and Google’s Deepmind Gemini 2.0, is increasing the importance of testing time-scale. Since this model requires considerable calculation resources, it emphasizes the need for companies to expand their computing functions to support advanced AI reasoning tools.
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