Doing Better with Less: LLM 2.0 for Enterprise
Standard LLMs are trained to predict the next tokens or missing tokens. It requires deep neural networks (DNN) with billions or even trillions of tokens, as highlighted by Jensen Huang, CEO of Nvidia, in his keynote talk at the GTC conference earlier this year. Yet, 10 trillion tokens cover all possible string combinations; the vast […]
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What is LLM 2.0?
LLM 2.0 refers to a new generation of large language models that mark a significant departure from the traditional deep neural network (DNN)-based architectures, such as those used in GPT, Llama, Claude, and similar models. The concept is primarily driven by the need for more efficient, accurate, and explainable AI systems, especially for enterprise and […]
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LLMs – Key Concepts Explained in Simple English, with Focus on LLM 2.0
The following glossary features the main concepts attached to LLM 2.0, with examples, rules of thumb, caveats, best practices, contrasted against standard LLMs. For instance, OpenAI has billions of parameters while xLLM, our proprietary LLM 2.0 system has none. This is true if we consider a parameter as a weight connecting neurons in a deep […]
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