LLM (Large Language Model) definition
In simple terms, an LLM (Large Language Model) is a type of artificial intelligence programme capable of recognising, translating and generating text, among other tasks. LLMs are a major advance in artificial intelligence that is radically transforming human-machine interactions. The definition of an LLM, or Large Language Model, is a complex system based on deep neural networks that are capable of performing complex tasks such as code generation, image or text analysis, and content generation with a level of sophistication very close to that of humans. According to a Grand View Research report, the LLM market has reached 5.6 billion and is expected to reach 35.4 billion by 2030, representing an annual growth rate of 37%. This illustrates a fundamental trend with increasingly significant adoption. A thorough understanding of these large language model technologies enables organisations to anticipate the potential impact on IT infrastructure and develop an innovative digital strategy for their business.
What is a large language model?
Origin and evolution of LLMs
Large language models emerged in 2018 with the first large-scale models. OpenAI's very first GPT-1 version marked the beginning of this development, based on BookCorpus, which contained approximately 7,000 books from the publisher Smashwords, or 985 million words. In 2018, Google's BERT integrated all of Wikipedia's English data in addition to the Book Corpus data, or 3.3 billion words.
Technological developments are keeping pace with this significant growth. Transformer architectures make it easier to process sequences in parallel, which was not the case with previous sequential recurrent networks.
With recurrent use, the costs of training a model are falling significantly.
This implies a strong and continuous democratisation of computational LLMs, promoting the emergence of diverse ecosystems. In addition, the wide variety of models, including open source and proprietary solutions such as GPT-4, creates a positive competitive dynamic. Innovation is increased by this diversification and barriers to entry are lowered for organisations wishing to experiment with or adopt LLMs.
Fundamental principles of LLMs
LLMs are based on advanced mathematical expressions that transform sequences of numbers into relevant text predictions. A lexical field analysis converts words into numbered lists using techniques such as byte-pair encoding (BPE). Tokenisation maximises text compression. This means that a common expression can be encoded as a single token rather than multiple tokens, reducing computation time. For example, the GPT-4o version encodes the Gujarati language with more than four times fewer characters.
With the transformer, these models are structured around attention mechanisms that capture the complex relationships between distinct words. The global attention capacity separates LLMs from previous approaches that were limited to restricted contexts. Thus, the latest models such as Claude Sonnet 4 process 200,000 tokens simultaneously, while GPT-4o processes 128,000 tokens, which promotes consistency across large documents.
Training models in an autoregressive manner allows them to predict the next token probabilistically based on the previous context. Although simple at first glance, the generative approach promotes the development of highly sophisticated skills: contextual understanding, logical reasoning and textual creativity. The numerous parameters ensure that this linguistic knowledge, derived from particularly dense corpora, is stored.
Concrete day-to-day applications
Applications using a conversational approach are the most visible showcase for LLMs. ChatGPT, Claude, Gemini and DeepSeek are profoundly changing digital assistance with natural, contextual interactions. Virtual AI agents go beyond traditional chatbots with their ability to maintain coherent conversations and adapt to the user's communication style and the instructions set in the prompt.
Automated content generation is a game-changer for creative and communication-based professions. Writing articles, creating promotional videos, editing summaries, and multilingual translation are less time-consuming because they can potentially be automated. The explanation of LLM as a differentiating factor for productivity stimulates the creativity of professionals to focus on strategy rather than repetitive or low-value-added tasks.
Sentiment analysis and textual classification allow the contextual understanding of LLMs to be fully exploited to manage the processing of large document collections. Customer review analysis, fraud detection, competitive intelligence and content moderation are all benefiting from the rise of AI and improved models. What's more, the greater accuracy compared to standard statistical methods is another reason to adopt AI in the most critical business processes and applications.
How large language models work
Neural network architecture
LLMs operate based on a series of fundamental principles derived from machine learning and neural architectures. LLMs are structured using transformer architecture based on encoders and decoders, with self-attention mechanisms. This parallelises the processing of entire sequences, unlike sequential recurrent architecture. This multi-parameter attention captures types of linguistic relationships: pragmatic, semantic and syntactic. The richness of representation allows for very satisfactory levels of performance on a variety of linguistic tasks. LLMs are trained on huge text corpuses, often consisting of trillions of words.
Multiple normalisation layers and residual connections stabilise the training of very deep networks, sometimes with hundreds of layers. Depth facilitates the hierarchisation of representations: from characters to words, from sentences to paragraphs, and from simple concepts to the most complex reasoning. The increased volume of parameters correlates with improved performance, which implies fierce competition between LLMs.
Autoregressive models vs encoder-decoder models
Autoregressive AI models generate text token by token, conditioning the next prediction on the previous one. This is used by GPT and other models, and excels at generating creative content and text. These models offer consistent narrative and style, making them suitable for automatic text generation and conversational assistance tasks.
The ‘encoder-decoder’ architecture separates comprehension (the encoder) from generation (the decoder), which optimises text transformation. BERT adopts this bidirectional approach by simultaneously analysing the left and right context of each token. The global view allows for better textual understanding, but this is at the expense of fluid generation and favours analytical applications such as classification, entity extraction, or question answering.
A hybrid mix of these approaches combines their advantages. The T5 (Text-to-Text Transfer Transformer) model created by Google reformulates linguistic tasks as conditional generation problems. This simplifies training and cross-task generalisation. Unified models such as PaLM 2 (developed by Google), used in multilingual applications, illustrate this convergence towards versatile architectures.
LLM training process
Pre-training brings together most of the computational resources by ingesting massive text corpora via self-supervised learning. This establishes the fundamental linguistic representations: factual knowledge, grammar, reasoning models and vocabulary. The scale of these corpora, with Common Crawl, for example, exceeding 50 billion web pages, requires distributed infrastructures mobilising a very large volume of GPUs over many months.
Supervised adjustment, also known as fine tuning, focuses generic models on specific domains and/or tasks. This makes it possible to use high-quality annotated datasets to refine and validate the desired behaviours. The RLHF technique (Reinforcement Learning from Human Feedback) allows alignment with human preference models: safety, usefulness and truthfulness. This determines the social acceptance of deployed machine learning systems.
Continuous optimisation increases the relevance of post-deployment models through user feedback and usage data. This continuous improvement loop characterises LLM cloud services. At UltraEdge, we also implement these best practices and develop AI agents to optimise the cost-effectiveness of our 250 data centres and 7 IX data centres.
Incremental updating of LLMs avoids the need to retrain the model each time and reduces computational costs. Technological flexibility increases innovation while improving responsiveness to newly identified needs.
Natural language processing techniques
Vector embedding allows each word to be encoded within multidimensional spaces. This preserves semantic relationships. This continuous representation replaces symbolic encodings and allows models to capture different linguistic nuances. Semantically similar expressions are represented in adjacent locations in the vector space, facilitating generalization and analogy. Representation using this semantic geometry establishes the context comprehension capabilities of LLMs.
Dynamic weighting of the importance of each contextual element is performed according to the current task. Selective attention mimics human cognition by focusing on the most relevant information. Multi-lever, it simultaneously captures local and global dependencies, from grammatical agreement rules to anaphoric references.
Regularization techniques anticipate overfitting on limited corpora. Parameters such as dropout, early stopping, and weight decay preserve the ability to generalize despite the intrinsic complexity of the architecture. Training corpora can be enriched by augmenting data through paraphrasing, back translation, or synthetic generation. These advanced techniques partially compensate for potential cognitive biases and the limitations of natural datasets.
LLM, cloud computing and IT infrastructure
GPU infrastructure and specialized accelerators: TPUs, ASICs
LLM training and inference rely heavily on specialized hardware acceleration. Nvidia GPUs dominate this market with their Ampere and Hopper architectures optimized for tensor computations. An LLM training session typically mobilizes thousands of A100 or H100 cluster units, representing colossal investments of hundreds of millions of dollars.
Computational intensity directly influences infrastructure strategies for organizations using these technologies. For example, TPUs (Tensor Processing Units) such as those from Google offer an alternative optimized specifically for machine learning. ASIC (Application-Specific Integrated Circuits) chips outperform general-purpose GPUs on transformer workloads. The most typical matrix operations of LLMs are greatly accelerated by systolic architecture and mixed precision (bfloat16), with minimal memory usage. Optimizing the performance-to-power ratio becomes simplified during large-scale deployments!
Greater technological diversification drives innovation while reducing dependence on a single supplier. The LLM infrastructure of the future will likely integrate optimized heterogeneous architectures while taking into account the lifecycle of different models.
Roles of LLMs in Edge and hybrid architectures
LLM inference, i.e., drawing conclusions from new data, is gradually adapting to the requirements of Edge computing through techniques such as:
● Quantization
This reduces the numerical precision of parameters—from 32 to 8 bits—while lowering memory footprint and computing requirements.
● Knowledge distillation
This transfers the intrinsic capabilities of the largest language models to more compact versions that can be easily deployed on site.
This facilitates the execution of LLMs, and in our UltraEdge data centers, we are able to run them on our various equipment. The hybrid architecture combines local processing and the cloud depending on the complexity of the requests. The simplest tasks are performed locally for minimal latency, while more complex reasoning is loaded onto external infrastructures with greater capacity. The intelligent distribution operated in our Edge data centers maximizes the user experience while controlling computational costs. The cloud computing security of hosting providers or data center operators has a lasting impact on architecture choices.
Thus, the confidentiality of localized data is preserved by a distributed architecture across multiple Edge sites that brings together several models. Our approach is in line with regulatory and sovereignty constraints, with shared learning. It should be noted that the two techniques of “differential privacy” and “secure aggregation” guarantee the protection of the most sensitive information. The approach prefigures a move towards even greater flexibility and autonomy for LLM infrastructures.
Use cases for LLM applications in data centers
Hosting LLM applications for various uses in data centers allows the computational power of these models to be exploited. For example, machine translation services may require millions of queries every day, requiring highly advanced multilingual models. Real-time sentiment analysis via social media allows opinion polls to be conducted for brands or public figures.
The critical nature of applications and services requires very high availability, ultra-low latency, and automated scalability. Thus, the generation of personalized content or the creation of AI-powered chatbots leverages LLMs to create new user experiences. This logic of personalization, which we see in product recommendations, contextual articles, and targeted advertising campaigns, is taken to the extreme. In addition, CRM and marketing platforms are directly integrated into data centers. And the creative process is boosted while maintaining consistency and brand-specific messaging.
Systems featuring sophisticated conversational agents can address even the most complex issues by accessing comprehensive knowledge databases. The generation of customized solutions is increased, significantly reducing the need for human experts except in rare cases. Greater responsiveness is achieved and customer satisfaction is optimized. More time is freed up for experts and technicians at the data center operator or for the customer, maximizing the production of high-value tasks.
Challenges and limitations of LLMs
Bias, ethics, and cognitive limitations of LLMs
Cognitive biases in AI models, with potential hallucinations, remain a significant concern in the deployment of LLMs. Indeed, each model has the potential to reproduce or even amplify certain biases present in the training corpus, sometimes resulting in unequal treatment or the reproduction of socio-cultural stereotypes. For example, gender bias in professional representation could accentuate the representation of men in certain occupations, or even include racial stereotypes in automated decisions. Effective detection and correction of these biases requires a multi-pronged approach that combines technical expertise, human feedback, and social sciences.
These cognitive hallucinations are intrinsic biases of LLMs that can generate completely erroneous information, and do so with complete confidence! The invention of facts that appear plausible but are in fact inaccurate complicates or even prevents their use in the most critical contexts or sectors, such as healthcare.
It should be noted that the ambiguity of LLM decisions can hinder their full adoption in the most regulated areas, which require traceability of reasoning. The more opaque the attention mechanisms are and the greater the complexity of representations, the more difficult it is to interpret the choices made by AI algorithms. In short, it is a computational black box that can raise ethical questions related to legal liability, and is therefore sensitive in applications or services involving humans.
LLMs also face scalability and cost issues. As language models become larger, computing and memory requirements increase exponentially, making it difficult to train and deploy extremely large models. The costs associated with training LLMs are high due to the need for specialized hardware such as GPUs and robust infrastructure.
Energy consumption and environmental impact
The rise of LLMs and their energy consumption is generating costs that are virtually unmatched. The considerable energy consumption required for continuous training on massive data sets represents a significant cost issue. GPT-3 reportedly required 1,287 MWh during its training, equivalent to 552 tons of CO2. This particularly large carbon footprint raises questions about the sustainability of this frantic race in the artificial intelligence sector. Architecture optimization and advances in hardware efficiency by hosting providers such as UltraEdge make it possible to decouple performance and consumption. The daily inference of the most popular LLM services represents a significant energy cost. According to a study by the Epoch website, ChatGPT's training cycles consumed 20 to 25 megawatts each for approximately three months, which is equivalent to 20,000 households in the United States.
This demand requires cloud infrastructures adapted to manage peak loads, which impacts the carbon footprint. Optimizing AI models for inference and using green energy in data centers remain key levers for optimization and carbon footprint reduction.
The obsolescence of IT hardware and equipment can also exacerbate the environmental impact through the combined increase in production and recycling of the most complex components. Given that GPUs and TPUs evolve very quickly, this can render certain previous generations of hardware or devices obsolete. A circular economy and the reconditioning of resources within the context of the connected city are areas for improvement that are worth exploring.
Regulations, digital sovereignty, and LLM transparency
Specialized regulations are gradually being introduced to oversee the deployment and potential side effects of LLMs. In June 2024, the European Union adopted the first global rules on artificial intelligence. These mainly involve transparency requirements, risk assessment, and improved algorithmic governance. These regulatory constraints will potentially influence the deployment of LLMs, favoring companies with significant legal and technical resources.
The issue of sovereignty raises questions about the dependence developed by models outside the EU, such as ChatGPT in the United States and DeepSeek in China. The French and European Mistral initiative demonstrates a desire to stand out and be locally autonomous. Inter-state fragmentation and the race for AI strengthen strategic resilience.
Greater transparency of AI models requires disclosure of the training methodologies associated with LLMs, and even the corpora used and the techniques for improving them.
This requires striking a balance between the transparency required by the European AI Act and intellectual property, which differs depending on the geographical jurisdiction. It also requires more frequent regulations and external audits in order to respond effectively to these challenges.
Future prospects for large language models
LLMs are constantly evolving, with ongoing research efforts to improve their performance, reliability, and accessibility.
Expected technological improvements
The computational efficiency of LLMs, and in particular their ability to solve problems quickly, will contribute to the rapid democratization of these models. Sparse architectures make it possible to activate subsets of parameters according to a specific context and reduce computational requirements. Techniques related to “Mixture-of-Experts” (MoE) make it possible to divide different parts of the model into smaller domains or networks. The model improves its overall performance at a lower cost!
Continuous learning allows LLMs to adapt and be flexible without the need for complete retraining. Cognitive plasticity brings models closer to natural learning, as an individual would do. The inclusion of incremental fine-tuning and transfer learning techniques complement this continuous adaptation. The integration of external knowledge via vector bases enriches factual reasoning capabilities without modifying the architecture.
The native multimodality of AI agents ensures visual, auditory, and textual processing of unified architectures. This already makes it possible to generate rich content such as annotated videos, interactive presentations, and more immersive experiences. Generative video models, such as OpenAI's Sora, herald this convergence towards increasingly creative AI. And open up new use cases for sectors such as communication, entertainment, and education.
Integration into various industrial sectors
The healthcare and Big Pharma sectors will leverage LLMs for more advanced diagnostic assistance, detailed analysis of patient records, and clinical report generation. However, rigorous validation remains required for the most critical applications. In addition, increasingly personalized treatments based on analysis of up-to-date scientific literature from multiple countries will transform the traditional approach to medicine.
FinTech and certain banking players are accelerating the deployment of LLMs to improve risk analysis and anti-fraud mechanisms and expand the use of AI chatbots for customer support. The criticality and massive scale of transactions and communications require the early detection of potentially suspicious patterns or investment opportunities. The regulatory compliance required by the financial sector calls for innovative approaches to associated audits. For the IT and data center sector, back-office process automation will free experts and technicians from recurring, time-consuming, and low-stakes tasks.
Education will benefit from fully adaptive AI tutoring that individualizes learning paths according to predefined rhythms and styles. The intelligence of these AI-powered systems will more effectively resolve specific difficulties by offering exercises related to the topic. In addition, multimodal learning combining visuals (e.g., infographics) and interactions will improve comprehension.
How do we deploy AI at UltraEdge?
UltraEdge integrates LLMs into its various Edge infrastructures, and its ultra-dense network of over 250 sites and 7 IX data centers brings artificial intelligence closer to end users, while improving performance and significantly reducing latency.
The distributed architecture maximizes the local inference capabilities of optimized LLMs and reduces dependence on cloud connections. Intelligent orchestration allows us to distribute loads according to the complexity of different requests: local processing for the simplest tasks, and migration to IX data centers for complex reasoning.
Thus, optimizing LLMs with the imperatives of an Edge infrastructure is a constant focus of innovation. With quantization techniques, parameter pruning, and knowledge distillation, it is possible to benefit from the technological contributions of LLMs while taking into account local and even energy constraints of IT equipment. Greater model customization improves the contextual relevance of our hosting offerings while complying with local standards and ensuring data confidentiality.
This contributes to the UltraEdge vision of benefiting from LLM innovations with greater resilience, security, and locally boosted performance.