How the New Breed of LLMs is Replacing OpenAI and the Likes

Before diving into the architecture of new LLMs, let’s first discuss the current funding model. Many startups get funding from large companies such as Microsoft, Nvidia or Amazon. It means that they have to use their cloud solutions, services and products. The result is high costs for the customer. Startups that rely on vendor-neutral VC funding face a similar challenge: you cannot raise VC money by saying that you could do better and charge 1000x less. VC firms expect to make billions of dollars, not mere millions. To maintain this ecosystem, players spend a lot of money on advertising and hype. In the end, if early investors can quickly make big money through acquisitions, it is a win. What happens when clients realize ROI is negative, is unimportant. As long as it does not happen too soon! But can investors even achieve this short-term goal?

The problem is compounded by the fact that researchers believe deep neural networks (DNN) are the panacea, with issues simply fixed by using bigger data, multiple transforms to make DNN work, or front-end patches such as prompt engineering, to address foundational back-end problems. Sadly, no one works on ground-breaking innovations outside DNNs. I am an exception.

Read the full article, including documentation and Python code about my free energy-efficient, open-source specialized multi-LLM (local implementation, enterprise version), here .