Differences between transformer-based AI and the new generation of AI models

I frequently refer to OpenAI and the likes as LLM 1.0, by contrast to our xLLM architecture that I present as LLM 2.0. Over time, I received a lot of questions. Here I address the main differentiators.

First, xLLM is a no-Blackbox, secure, auditable, double-distilled agentic LLM/RAG for trustworthy Enterprise AI, using 10,000 fewer (multi-)tokens, no vector database but Python-native, fast nested hashes in its original version, and no transformer to generate the structured output to a prompt. The final, chat-like answer (optional) relies on a small, double-distilled DNN and other techniques. While by no means simplistic, the architecture, at least at a high level, appears more intuitive and simpler. The details are in the numerous components, each with its own set of parameters. The main part has zero parameters (also called weights) as it does not rely on DNNs. Here DNN means deep neural network, and parameter have a different meaning in our context, similar to parameters in physics models.

I now share the main innovations focusing on the technical aspects. Then I provide answers to frequently asked questions.

Main differentiators

xLLM stands for “Extreme LLM”, by analogy to extreme rock climbing, best illustrated by Reinhold Messner’s solo ascension of Everest in 1980 with no oxygen, at a fraction of the time, cost and team size. Likewise, the first implementation of xLLM was on a small laptop with no GPU, to prove feasibility even with large corpuses, favoring intelligent design and optimized resource allocation over brute force.  The main advantages are as follows.

• Radically different architecture combining highly efficient, much better proprietary RAG, together with small specialized LLM with tiny DNN, and proprietary, high-quality agents. Very fast.
• Unmatched security (you have control over all components), no external API call (home-made), response evaluated using better standards than classic benchmarking (we favor exhaustivity, quality of relevancy scores over “LLM as a judge”, see here)
• Two types of responses: chat or structured output (hallucination-free, text shown “as is” as summary boxes with relevancy scores, categories, tags, timestamps, chunk size, suggested alternate prompts, very precise, accurate references to source). Both types of response available from comprehensive UI (not just a prompt or chat box)
• Much better UI than competitors. Also, possibility to browse the corpus from the UI, search by recency, exact or broad match. Hierarchical chunking, with parent and child chunks of various sizes.
• Standard response based on very concise distilled structured output rather than large blocks of text as in other models. Uses small specialized distilled DNN and other advanced NLP techniques, based on your corpus only. Weights close to 0 are eliminated (that’s 99% of the 40B weights found in other LLMs). Thus the term double distilled.
• no-Blackbox, no large DNN hard to train. Faster onboarding, easy to fine-tune even in real-time.
• Customized to your own data including the high-level parameters and auxiliary data such as stemmer, stopwords or acronym dictionary
• Zero weights instead of 40B / 10,000x fewer (multi-)tokens, no GPU. Instead, we have 20 top-level parameters to fine-tune various components (scores, stemmer, PMI, distillation, hierarchical chunking)
• Relevancy and trustworthiness scores — the latter assess the quality of the input source (we don’t make up answers).
• Replicable response. Ask the same question twice, you get the same answer. Makes debugging easier.
• Work on PDFs, your data lake, public or private Internet data. All converted to JSON-like format, including the retrieved structure, taxonomy and detected graph knowledge. Possibility to augment/strengthen the underlying structure with auto-tagging and categorization.
• Detect information missing in your corpus thanks to relevancy scores and exhaustivity: help you improve your corpus, add entries to your corpus
• Fine-tune high-level parameters (stemmer, PMI, chunking, distillation and so on) in real-time. xLLM version available for in-memory usage (in-memory LLM with very fast, proprietary DB — no vector DB) or implemented on the edge (cell phone, IOT sensors)
• Proprietary agents: best tabular data synthesizer (NoGAN, useful in fraud detection), predictive analytics on retrieved documents, taxonomy builder & auto-indexing, pattern detection (medical data)
• Multi-tokens of various types: either found in context (titles, category name, and so on) or regular text. For instance, ‘real estate San Francisco’ is one (multi-)token. So is ‘real estate’. We have fewer than 1 million multi-tokens, compared to billions for transformer-based models (pretty much what all competitors rely upon).
• Other: DNN & data watermarking to protect against unauthorized use, resistant to tampering.

Finally, our methodology is related to the symbolic approach: one of our components is ontology retrieval and augmentation. Indeed, we offer auto-indexing, auto-tagging, text clustering, cataloguing and taxonomy building, as one of our specialized agents.

Questions and answers

Is it scalable? 

When you use at least 10,000 fewer tokens, scaling on premises (or on my own old laptop, for that matter) is not an issue. The backbone (all you need to retrieve to build a structured response) fits in memory. Now your 500,000 PDFs need to be stored somewhere of course, can be on some cloud, but the RAG process will tell exactly where the source used to build the response is located, and fetch it as needed. In short, what is in-memory is the complex hierarchical multi-index structure.

How can it be so fast?

We have our proprietary DB architecture which (in one version) is nested hashes, thus native to Python. Multi-tokens and multi-token association tables, as well as un-stemmer DB, are small enough, given a specific corpus, to stay in memory with very fast retrieval. Small enough that table redundancy is used to accelerate retrieval, such as some index tables and their transposed version both stored separately while containing the same data organized in different ways.

What about the cost?

We use multi-tokens consisting of multiple words. We typically have fewer than a million compared to 40B models. And we don’t charge by (multi-)token. In short, we are 10,000 times more compact, faster, more accurate, yet much less expensive than all other models. Especially in terms of required infrastructure.

How do you compare to (say) the new Google RAG?

What Google just released (RAG API) we do it in-memory without vector database, without embeddings or cosine similarity. Much faster, augmented with unstemmer / acronym dictionaries specialized for corporate lingo. Trustworthiness and relevancy scores attached to the response (structured output or chat-like). Hierarchical chunking. Exhaustive, relevant results with precise references to corpus elements. On-premises.

Benefits over other specialized Enterprise AI models (sLLMs)? 

Works on database, Internet, PDF repositories and other types of corpuses, or combination of all. Input data turned to proprietary xLLM (text) format. Real-time fine-tuning with a few dozen parameters. No Blackbox, no GPU. Predictive analytics on retrieved tables. Browse your corpus from the results to a prompt, and try alternate prompts that our response suggests, in one click. Relative font size used to build contextual elements using our font intelligence component. A few millions of corpus-related multi-tokens consisting of several words rather than billions of tiny tokens, to increase efficiency / reduce costs. Contextual, standard and other types of multi-tokens each with specific weight depending on type and other metrics. A UI that beats any competitor (not just a search box or chat-like window).

Other interesting features?

Search by recency, exact or broad match, negative keywords. We call it trustworthy, auditable AI, the most secure architecture on the market built by experts in cybersecurity, LLMs, and search/RAG technology.

Is this new stuff developed just 2 years ago to capitalize on AI?

We started to work on our technology before Google was born. Better RAG is one of the many components of our xLLM architecture. Contact us at vincent@bondingai.io for demo.

Related articles and presentations

• Video: the LLM 2.0 Revolution – link.
• How to Get AI to Deliver Superior ROI, Faster – link.
• Benchmarking xLLM and Specialized Language Models – link.
• Doing Better with Less: LLM 2.0 for Enterprise – link.
• How to Design LLMs that Don’t Need Prompt Engineering – link.
• From 10 Terabytes to Zero Parameter: The LLM 2.0 Revolution – link.
• 10 Must-Read Articles and Books About Next-Gen AI in 2025 – link.
• Watermarking, distillation & Forensics for Deep Neural Networks – link.

About the Author

Vincent Granville is a pioneering GenAI scientist, co-founder at BondingAI.io, the LLM 2.0 platform for hallucination-free, secure, in-house, lightning-fast Enterprise AI at scale with zero weight and no GPU. He is also author (Elsevier, Wiley), publisher, and successful entrepreneur with multi-million-dollar exit. Vincent’s past corporate experience includes Visa, Wells Fargo, eBay, NBC, Microsoft, and CNET. He completed a post-doc in computational statistics at University of Cambridge.