From 10 Terabytes to Zero Parameter: The LLM 2.0 Revolution
In this article, I discuss LLM 1.0 (OpenAI, Perplexity, Gemini, Mistral, Claude, Llama, and the likes), the story behind LLM 2.0, why it is becoming the new standard architecture, and how it delivers better value at a much lower cost, especially for enterprise customers. 1. A bit of history: LLM 1.0 LLMs have their origins in tasks such as search, translation, auto-correct, next token prediction, keyword associations and suggestion, as well as guessing missing tokens or text auto-filling. Auto-cataloging, auto-tagging, auto-indexing, text structuring, text clustering, and taxonomy generation also have a long history but are not usually perceived as LLM technology, except indirectly as knowledge graphs and contextual windows. Image retrieval and processing, video and sound engineering are now part of the mix, leveraging metadata and computer vision, and referred to as multimodal. Solving tasks such as mathematical problems, filling in forms, or making predictions are being integrated via agents frequently relying on external API calls. For instance, you can call the Wolfram API for math: it has been around for over 20 years to automatically solve advanced problems with detailed step-by-step explanations. However, LLMs’ core engine is still transformers and deep neural networks, trained on predicting next tokens, a task barely related to what modern LLMs are used for these days. After years spent in increasing the size of these models, culminating with multi-trillion parameters, there is a realization that “smaller is better”. The trend is towards removing garbage via distillation, using smaller, specialized LLMs to deliver better results, as well as using better input sources. Numerous articles now discuss how the current technology is hitting a wall, with clients complaining about lack of ROI due to costly training, heavy use of GPU, security, interpretability (Blackbox systems), and hallucinations – a liability for enterprise customers. A key issue is charging clients based on token usage, favoring multi-billion token databases with atomic tokens over smaller token lists with long contextual multi-tokens, as the former commands more revenue for the vendors, at the expense of ROI and quality for the client. 2. The LLM 2.0 revolution It has been brewing for a long time. Now it is becoming mainstream and replacing LLM 1.0, for its ability to deliver better ROI to enterprise customers, at a much lower cost. Much of the past resistance towards its adoption lied in one question: how can you possibly do better with no training, no GPU, and zero parameter? It is as if everyone believed that multi-billion parameter models are mandatory, due to a long tradition. However, this machinery is used to train models on tasks irrelevant to the purpose, relying on self-reinforcing evaluation metrics that fail to capture desirable qualities such as depth, conciseness or exhaustivity. Not that standard LLMs are bad: I use OpenAI and Perplexity a lot for code generation, writing my investor deck, and even to answer advanced number theory questions. But their strength comes from all the sub-systems they rely upon, not from the central deep neural network. Remove or simplify that part, then you get a product far easier to maintain and upgrade, costing far less in development, and if done right, delivering more accurate results without hallucination, without prompt engineering and without the need to double-check the answers: many times, OpenAI errors are quite subtle and can be overlooked. Good LLM 1.0 still saves a lot of time but requires significant vigilance. There is plenty of room for improvement, but more parameters and Blackbox DNNs have shown their limitations. I started to work on LLM 2.0 more than two years ago. It is described in detail in my recent articles: See also my two books on the topic: It’s open source, with large Git repository here. See also a web API featuring the corpus of a Fortune 100 company where it was first tested, here. Note that the UI is far more than a prompt box, allowing you to fine-tune intuitive front-end parameters in real time. In the upcoming version (Nvidia), you will get a relevancy score attached to each entity in the results, to help you judge the quality of the answer. Embeddings will help you dig deeper by suggesting related prompts. It will also allow you to choose agents, sub-LLMs or top categories, negative keywords, return recent results only, and more. 3. An interesting analogy Prior to LLMs, I worked for some time on tabular data synthetization, using GANs (generative adversarial networks). While GANs work well in computer vision problems, their performance is a hit-and-miss for synthesizing data. It requires considerable and complex fine-tuning depending on the real data, significant standardization, regularization, feature engineering, pre- and post-processing, and multiple transforms and inverse transforms to perform decently on any data set, especially those with multiple tables, time stamps, multi-dimensional categorical data, or small datasets. In the end, what made it work is not GAN, but all the workarounds built on top of it. GANs are unable to sample outside the observation range, a problem I solved in this article. The evaluation metrics used by vendors are poor, unable to capture high-dimensional patterns, generating false positives and false negatives, a problem I solved in this article. See also my Python library, here, and web API, here. In addition, vendors were producing non-replicable results: running GAN twice on the same training set produced different results. I actually fixed this, designing replicable GANs, and of course everything I developed outside GAN also led to replicability. In the end, I invented NoGAN, a technology that works much faster and much better than synthesizers that rely on deep neural networks. It is also discussed in my book published by Elsevier, available here. The story is identical to LLM 2.0, moving away from DNNs to a far more efficient architecture with no GPU, no parameter, no training, fast and easy to customize with explainable AI. Interestingly, the first version of NoGAN relied on hidden decision trees, a hybrid technique sharing similarities with XGBoost, and that I created for scoring unstructured text data as far back as 2008. It has its own patents and resulted in my first VC-funded startup,
xLLM: New Generation of Large Language Models for Enterprise
I get many questions about the radically different LLM technology that I started to develop 2 years ago. Initially designed to retrieve information that I could no longer find on the Internet, not with search, OpenAI, Gemini, Perplexity or any other platform, it evolved to become the ideal solution for professional enterprise users. Now agentic and multimodal, automating business tasks at scale with lightning speed, consistently delivering real ROI, bypassing the costs associated to training and GPU with zero weight and explainable AI, tested and developed for Fortune 100 company. So, what is behind the scenes, how different is it compared to LLM 1.0 (GPT and the likes), how can it be hallucination-free, what makes it a game changer, how did it eliminate prompt engineering, how does it handle knowledge graphs without neural networks, and what are the other benefits? In a nutshell, the performance is due to building a robust architecture from the ground up and at every step, offering far more than a prompt box, relying on home-made technology rather than faulty Python libraries, and designed by enterprise and tech visionaries for enterprise users. Contextual smart crawling to retrieve underlying taxonomies, augmented taxonomies, long contextual multi-tokens, real-time fine-tunning, increased security, LLM router with specialized sub-LLMs, an in-memory database architecture of its own to efficiently handle sparsity in keyword associations, contextual backend tables, agents built on the backend, mapping between prompt and corpus keywords, customized PMI rather than cosine similarity, variable-length embeddings, and the scoring engine (the new “PageRank” of LLMs) returning results along with the relevancy scores, are but a few of the differentiators. Keep in mind that trained models (LLM 1.0) are trained to predict the next tokens or to guess missing tokens but not trained to accomplish the tasks they are supposed to do. The training comes with a big price tag: billions of parameters and a lot of GPU. The client ends up paying the bill. Yet the performance comes from all the heavy machinery around the neural networks, not from the neural networks themselves. And model evaluation fails to assess exhaustivity, conciseness, depth, and many other aspects. All the details with case studies, datasets, and Python code are in my new book, here, with links to GitHub. In this article, I share section 10.3, contrasting LLM 2.0, to LLM 1.0. Several research papers are available here. LLM 2.0 versus 1.0 I broke down the differentiations into 5 main categories. Due to the innovative architecture and next gen features, xLLM constitutes a milestone in LLM development, moving away from the deep neural network (DNN) machinery and its expensive black-box training and GPU reliance, while delivering more accurate results to professional users, especially for enterprise applications. Here the abbreviation KG stands for knowledge graph. 1. Foundations 2. Knowledge graph, context 3. Relevancy scores, exhaustivity 4. Specialized sub-LLMs 5. Deep retrieval, multi-index chunking