Given the high costs and slow speed of training large language models (LLMs), there is an ongoing discussion about whether spending more compute cycles on inference can help improve the performance of ...

Data scientists use machine learning to make decisions based on a virtual flood of data, but they must consider two distinctly different parts of the process: training and inference. Training tells a ...

The standard guidelines for building large language models (LLMs) optimize only for training costs and ignore inference costs. This poses a challenge for real-world applications that use ...

While the tech world obsesses over headlines about the $100 million price tag to train GPT-4, the real economic story is happening in inference: the ongoing cost of actually running AI models in ...