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AI revolution: secure your competitive edge with on-premise LLM deployment

insight
February 08, 2024
9 min read

Author

EugenEugen Rosenfeld
A CTO & a Solution Architect in Life Sciences at Nagarro. He has more than 20 years experience in different programming languages, technologies and business domains.

 

The AI landscape has cracked open. Generative Pre-trained (GPT) and Large Language Models (LLM) have moved from the labs into the mainstream. This tidal wave of Generative AI innovation will reshape every industry, creating diverse capabilities that will make it— key to growth in the years to come.

To help you keep up with the rapid pace of change, this article aims to give you an insight into the Generative AI revolution and help you make informed decisions about integrating LLMs into your business strategy for growth. Prioritize security and control of an on-premises environment first.

 

Brief note for the reader:

- Generative AI is a technique used to achieve a Large Language Model, so we will discuss LLM deployment in this article. 
- This article addresses the language capabilities of LLM to handle text-based scenarios exclusively. 

 

Highlights

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Big data has unleashed a powerful magic trick: turning data patterns into human words. Now we're taking it a step further with Generative AI capabilities to mimic, truly understand, and create a language that shapes the world around us. It's about more than just a new way to talk – it's about harnessing the power of language to change the game in every conceivable area. The GPT model is a breakthrough innovation with linguistic superpowers. 


Decoding the LLM model 

GPT- The most powerful Generative AI model. 

GPT is a large language model developed by OpenAI. It is a type of neural network that has been trained on a massive dataset of text and code. This training allows GPT to generate human-quality text, translate languages, write different kinds of creative content, and answer your questions in an informative way. 

To understand the technology, let's dissect the core components of GPT and explore how its capabilities can empower your organization. 


3 circles adding up to show the core components of GPT


Transformer  

The 'Transformer' is the blueprint of the AI model. It connects the building blocks of this AI and enables it to handle complex linguistic tasks by paying attention to key pieces of the input. Transformers break down language barriers and pave the way for smoother communication between humans and machines. 


Transformer refers to a Neuronal Network model developed by Google in 2017. It has revolutionized the world of language models ever since then. Think of it like a language processing wizard that’s what the Transformer architecture, the heart of GPT, essentially represents.  

These features of Transformer ensure its remarkable performance:

  • Encoder-decoder structure: The Transformer is an excellent translator and is often used in an encoder-decoder duo. The Encoder analyzes and compresses complex information into a compressed representation, while the Decoder seamlessly converts it back into a coherent output. This is particularly useful for tasks such as language translation. It also allows GPT to process large amounts of data and generate concise and relevant content. 

  • Parallel processing: Unlike sequential models, the Transformer is a parallel processing powerhouse that can process all parts of the sequence simultaneously, increasing efficiency and enabling it to handle longer, complicated language tasks. It's like having a team of linguists working together to analyze your data at lightning speed. 

  • Attention mechanism: The model pays attention to the different aspects of the input sequence, analyzing relationships and capturing dependencies within your data. Imagine GPT having a conversation with itself. Through this internal dialog, the model is able to capture complex nuances and generate outputs that are not only factually accurate but also consistent and coherent. 

  • Self-attention: The model pays attention to the different aspects of the input sequence, analyzing relationships and capturing dependencies within your data. Imagine GPT having a conversation with itself. Through this internal dialog, the model is able to capture complex nuances and generate outputs that are not only factually accurate, but also consistent and coherent. 

  • Versatility: The Transformer model is universally applicable and overcomes all limitations when processing certain types of sequential data. Summaries, question answering, and even creative writing become its playground, opening up limitless possibilities for your business applications.  

The encoder-decoder and the attention/self-attention mechanisms are the key features for the Transformer model are the. Its features provide the real power for any business and here's how: 
Scenario 1:

Vector-4

Imagine that you have to read a 2000-page book. Now, create a book summary that gives you the same information on just 10 pages. Wouldn’t this make the learning process easier? The Encoder-Decoder function allows the model to encode high-dimensional sequential information in a much smaller dimension and vice versa. Simply put, you can decompose and reassemble complex data precisely thanks to the Encoder-Decoder.

Scenario 2:

Vector-4

Suppose you need to understand abstract information that compresses a large area of information into just a few sentences. This can have a remarkably high complexity in terms of construction (like an overly complex mathematical function). Wouldn’t it be easier to break the sentence down into its component parts, get a detailed explanation for each of them, and thus facilitate the comprehension process?

The attention mechanism is a key component that allows the model to focus on different parts of the input sequence as it processes each element: 

  • Focusing on important parts: Imagine you are reading a sentence. Not every word is equally important for understanding the meaning. Attention mechanism helps the model focus more on a sentence's crucial words or parts. 
  • Weighted attention: Imagine that attention gives different words a certain weight. Important words are given a higher weighting, and less important words are given a lower weighting. In this way, the model pays more attention to the most important words when processing the individual words. 
  • Context understanding: By considering certain words in different positions, the model develops a better understanding of the context of a sentence. It is as if it considers the surrounding words when it wants to find out the meaning of a particular word.

Using these mechanisms, a Transformer model can “learn” very efficiently. By learning, we mean the ability to generate a mathematical function from the given input that statistically defines relationships between letters, terms, and even sentences.

Pre-Trained  

Before GPT generates a single word, it devours trillions of words and learns the rhythm of language. This "pre-training" fuels its contextual responses.

Imagine feeding it with a vast library of texts, from news articles to scientific journals. This extensive training equips the model with a pre-existing understanding of the world, so it's ready to learn and adapt to your specific needs with minimal fine-tuning. 

It’s like giving GPT a head start. 

In ML/AI, a neuronal network that is not trained is of no use. Understanding how the model was trained (what data was used for training) is especially important. If you need professional legal help, go to a lawyer, but if you have a medical condition, go to a physician. Both might have the same intellectual abilities but have been trained (educated)in different areas. 

The biggest strength of ML/AI is that you can have models that have been trained in multiple domains and have the same knowledge and even the ability to recognize and use correlations between domains (like Leonardo da Vinci). 

Generative

The "Generative" engine of GPT is a force of creation, churning out tailored solutions, from personalized scripts to innovative designs, pushing the boundaries of what's possible. 

This is where the GPT truly shines - it transforms from passive interpreter to creator. The term "Generative" refers to the model's ability to take a sequence of terms as inputs and identify and suggest the term with the highest probability of coming next. By adding this term to the input and repeating the process, you generate additional information in the given context. Simply, it analyzes your input, predicts the most likely continuation and generates new content – a compelling marketing campaign, a personalized customer response, or an insightful report. 

For instance, if you ask the question “How are…?”, the most common terms for continuation are:
Probability table for what term may come next
If you take the term with the highest probability (in our case, “you”), add it to the question, and feed it back into the model, you get the following probability table for the continuation:
Probability table for what term may come next
You can generate bigger and better structures by feeding more terms into the model. Remember that the model is able to identify the most relevant terms based on the given context, so you get a well-constructed phrase by selecting the ones with the highest probability.

Avoid the hallucination trap and the power of probability with— temperature control

Before we look at some applications, it is important to remember that probabilities are not absolute. Selecting words with very low probabilities may be grammatically correct, but it can veer into the absurd. As you can see from the example above, the model returns a list of terms as a continuation of the given input. 

Assuming we select words with low probability, we can generate semantically correct structures, but they become increasingly inaccurate or far removed from reality. This is called Hallucination, and the word selection's randomness is called Temperature. GPT offers a solution – "Temperature", that allows you to control the risk of hallucination to ensure that your generated content remains anchored in reality while still exploring creative possibilities. The higher the temperature, the more hallucinations occur. At a temperature of zero (always taking the term with the highest probability), a more accurate output is generated for the given input.


Matchmaking for success

In the past, this was difficult to achieve due to complexity, but today, LLM and GenAI are powerful tools that enable business to develop a wide range of applications that were not possible just a few years ago.  With improved natural language understanding and generation capabilities, these chatbots are able to drive business success by leveraging these "out of the box" capabilities.

Chatbots

In today's digital landscape, the customer experience is paramount, and AI-powered chatbots are quickly becoming central players in delivering exceptional interactions. But what if you could take your chatbot to the next level by giving it near-human understanding and responsiveness? LLM/GenAI plays a crucial role in the development of chatbots. LLMs, such as GPT-3, excel at processing and generating human-like text, making them valuable tools for creating sophisticated, context-aware conversational agents. 

Today's improved natural language understanding and generation capabilities, these chatbots are able to drive business success by leveraging these "out of the box" capabilities.

Man-chatting-with-a-chatbot-on-phone-1
Enhanced intelligence with Natural Language Understanding (NLU)

  • Intent recognition: LLMs can be used to improve intent recognition by training the chatbot on different data sets to identify user's intent with a high degree of accuracy, enabling more effective responses. Imagine a customer service chatbot that understands a user's question and addresses its intent, be it frustration, curiosity, or urgency.

  • Named entity recognition (NER): By leveraging LLM's understanding of context, developers can improve NER capabilities. This is critical for extracting relevant information from user input, such as names, dates, and locations, to ensure personalized and context-appropriate responses. 

Conversational brilliance with context-aware responses

  • Context retention: Put an end to the robotic, repetitive chatbot persona. LLMs inherently maintain the context of previous interactions, fostering a fluid and natural dialogue and ensuring responses are aligned with the ongoing dialogue. Imagine a chatbot returning to a previous product discussion or acknowledging a repeated request, creating a personalized and familiar user experience.

  • Dynamic content generation: Ditch those pre-programmed, generic responses. LLMs can dynamically generate content based on contextual cues, allowing chatbots to provide varied and relevant responses. This adaptability is important when it comes to dealing with different user inputs and having engaging conversations.   

Personalization and user engagement


  • User profiling: By leveraging the contextual understanding of LLMs, chatbots can build user profiles over time. This information can be used to personalize interactions to provide a tailored experience for individual users. Imagine a chatbot remembering a customer's preferred method of purchase or greeting them by name, adding a touch of human connection to the digital experience.

  • Emotion recognition: Go beyond words to understand emotions. LLMs can be fine-tuned to recognize and respond to user emotions expressed in text. This capability enables chatbots to provide empathetic and contextually appropriate responses, contributing to a more emotionally intelligent interaction.

Continuous learning

  • Adaptive learning: LLMs don't just learn during initial training; they continuously evolve. By incorporating user feedback and new data sets, chatbots can adapt to evolving language patterns and user preferences, ensuring they remain effective and relevant.

  • Feedback integration: LLMs enable a seamless feedback loop that allows your chatbot to learn from every interaction and refine its responses. This creates a self-optimizing system that gets better with every conversation.  

Deployment and scalability

Cloud-based solutions: LLMs are often cloud-based and offer scalability for chatbot applications. This ensures that chatbots can handle increasing user volumes without breaking interactions and scale seamlessly based on demand.

API integration

Most LLMs have readily available APIs to integrate into your existing chatbot platform. This streamlines the development process and allows developers to focus on building custom functionalities while leveraging the capabilities of the language model to speed up development timelines and maximize efficiency. 

Full text-search databases

Full-text search databases are specialized systems designed to efficiently search through the entire contents of text-based documents rather than just index and search specific fields such as titles or keywords. Think of these systems as powerful magnifying glasses that can scan entire text libraries at once to find what you need. 

A full-text search database is a lot more than an index of keywords in documents without contextual awareness. LLM/GenAI technology brings human-like intelligence to a full-text search system, ushering in a new era of efficiency and relevance for your business.  

Here is how LLMs enhance your search landscape.

Computer-screens-in-a-database
Natural Language Understanding (NLU) semantic search

Semantic search: LLMs can be used to understand the semantics of user queries, enabling more accurate and contextualized retrieval of documents. The model understands the meaning behind words and phrases, enabling a deeper understanding of user intent. For instance, if a user searches for "sustainable building materials", they will not only come across pages mentioning "wood" or "bamboo"." However, they will also find insights on lifecycle analysis and environmental impact.


Contextual relevance: Cut out the irrelevant noise that clutters your search results. With the contextual understanding of LLMs, document search systems can deliver results that go beyond keyword matching. The model takes into account the context in which terms are used, improving the relevance of search results and facilitating more precise information retrieval. Imagine searching for "market trends in AI" and finding articles on technical advances and reports on industry regulations, competitor analysis, and potential investment opportunities – all tailored to your business needs.

Document indexing and categorization

  • Content summarization: LLMs can generate concise summaries of documents that are helpful in creating informative snippets for search results. This improves user comprehension and allows quick decision-making based on easily digestible content.

  • Topic modeling: By analyzing the language patterns in documents, LLMs can help intelligently categorize and assign relevant topics or tags. This eliminates the need for manual classification and improves the organization of the document corpus, enabling a more intuitive and targeted search.

 

Multilingual support

  • Cross-language search: LLMs can handle multiple languages, so they enable efficient cross-language document search. Users can enter queries in their preferred language, and the model can bridge language gaps to retrieve relevant documents, opening the doors for global collaboration.

  • Language translation: The integration of LLMs for language translation allows the document search system to deliver results in the user's preferred language, improving accessibility and usability and thus removing language-based barriers to information access.

User interaction and feedback

  • Query expansion: LLMs can help expand user queries by suggesting additional relevant terms based on context. This helps users refine their search and obtain more comprehensive results.

  • User behavior analysis: Analyzing user interactions with search results using LLMs allows continuous improvement. By understanding user preferences and adapting search algorithms accordingly, the system can improve the overall search experience.

Context-aware ranking

Dynamic ranking: LLMs can contribute to dynamic result ranking by considering the entire document's context and the user's search history. This ensures that the most relevant documents are displayed at the top of the search results. 

Document generation

Document Generation in GPT refers to the ability of GPT models to create various types of text documents, ranging from emails and letters to code and creative text formats, such as poems, scripts, musical pieces, email, letters, etc. 

LLM/GenAI can help in the development of template-based document generators by providing advanced natural language generation capabilities such as:

Robot-working-on-computer
Template customization and Natural Language Generation (NLG)

  • Template adaptation: LLMs can dynamically adapt templates based on user input and requirements. This flexibility allows the creation of highly customized documents and ensures that the generated content matches specific requirements and preferences.

  • Dynamic content insertion: Leveraging LLMs, document generators can intelligently insert dynamic content into templates. This includes adapting language style, incorporating relevant data, and generating contextually appropriate text based on the template structure.

Natural Language Understanding (NLU)

  • User intent recognition: The inclusion of LLMs improves the document generator's ability to recognize the user's intent when specifying template parameters. This ensures that the generated documents accurately reflect the user's desired content and format.

  • Context-aware content: LLMs help create context-aware content by understanding the relationships between different template elements. This leads to more coherent and contextual document creation. 

Language fluency and style

  • Language fluency: LLMs excel at producing fluent and natural-sounding language. This capability improves the overall quality of document output and provides users with professionally written content that meets certain stylistic requirements.

  • Style customization: Document generators can leverage LLMs to customize the writing style of generated content to match specific preferences or industry standards. This ensures consistency and professionalism across various document types.

Multilingual support

Multilingual document generation: LLMs  equipped with multilingual capabilities allow documents to be created in different languages. This is particularly valuable for businesses or users operating in a global context to ensure accessibility and relevance for different language groups.

Contextual variable handling

Variable interpretation: LLMs contribute to accurately interpreting variables within templates and enable the dynamic substitution of placeholders with relevant information. This is crucial for the creation of personalized and contextual content. 

 

Continuous improvement

Feedback integration: Document generators can benefit from LLMs by integrating user feedback into the training process. This iterative learning approach helps improve the model's performance over time and refine the quality of the generated documents.

These are just a few base use cases, but in combination or by adding further functions in terms of images, audio, and video, we can set up complex solutions:

 

1: all-in-one knowledge repository 
2: simplification of business processes 
3: accelerate the setup of projects (regardless of the technology domain) 
4: code generation based on short descriptions and many more.

 

Some of these use cases can be developed in the cloud with native services (e.g. Azure OpenAI or AWS Bedrock), but we recommend considering the on-premises approach (or a custom cloud solution).



Charting your on-prem LLM deployment course

 

As IT leaders grapple to identify how to deploy Generative AI today, the key question looms – where to deploy these powerful tools in the boundless expanses of the public cloud or within the secure confines of your own on-premises domain? 

Most tech leaders respond with the standard “it depends” answer, which rarely provides actionable insights. The future of artificial intelligence (AI) holds enormous potential that can catapult your business to unimagined heights. This article addresses this very question. 


Why deploy LLM on-premise

The base use cases (chatbot, text search, image generation, etc.) can now be easily put together using cloud services. However, the complexity will exponentially increase if we aim for more complex solutions that combine all the above capabilities and add constraints such as security, privacy and custom business processes. In such situations, using standard services in the cloud may not be enough. 

Using an off-the-shelf or open-source model, where you develop, test, and tune your application on-premises, you can apply AI to your data and achieve greater processing efficiency while maintaining control over your data. 

When designing an LLM/GenAI-based solution for your or your client’s business, the following non-functional requirements (NFRs) often apply and can be covered by an on-premises solution:  

 

Data security and privacy

On-premises hosting gives you a higher level of control over your data. For industries with stringent data security and privacy regulations, such as finance or healthcare, hosting LLMs on-premises ensures that sensitive information stays within your physical or virtual boundaries.

Regulatory compliance

Certain industries like finance, healthcare, and government are subject to strict regulatory frameworks. On-premises hosting allows you to ensure compliance with industry-specific regulations by keeping sensitive data and language model processing under direct control.


Customized security measures

You may have specific security protocols and measures tailored to your infrastructure. On-premises hosting allows the implementation of customized security controls and ensures that the LLM is integrated into the existing security framework in a way that complies with your policies.

Network performance and latency

On-premises hosting can provide lower network latency, which is critical for applications that require real-time or near-real-time response. This is particularly important in scenarios where fast inference of language models is required for applications such as chatbots, virtual assistants, or real-time data analysis. 

Complete control over resources

Hosting LLMs on-premises gives you complete control over the infrastructure and resources used for the language model. This control is valuable for optimizing performance, managing resource allocation, and ensuring the model is responsive to your needs. 

Data residency requirements

You may be subject to legal or contractual obligations that require data to be stored in specific geographic regions. With on-premises hosting, you can meet data residency requirements without relying on external cloud providers. 

Cost predictability

While on-premises hosting may involve a higher initial investment, it offers long-term cost predictability. Having a fixed cost structure can be beneficial, especially if usage patterns are well-established and relatively constant.  

Offline access and redundancy

On-premises hosting ensures that the LLM remains accessible even when internet connectivity is limited or unreliable. You can also implement redundancy and failover mechanisms to increase system reliability. 

Protection of intellectual property

If you work with proprietary algorithms or business-critical models, hosting LLMs on-site can be a measure to improve intellectual property protection. It minimizes the exposure of sensitive models to an external cloud infrastructure.  

Strategic control over upgrades and maintenance

On-premise hosting provides you with strategic control over the timing and execution of upgrades, maintenance, and changes to the language model infrastructure. This level of control allows you to manage these processes in accordance with your operational schedules and requirements. 


You may be thinking that hosting LLM and GenAI-based solutions on-premises is difficult, and you are right. But with the right expertise and the use of platform accelerators, you can eliminate the complexity of the infrastructure setup covering the NFRs and concentrate only on the functional requirements.

If you decide to go this route, I recommend partnering with an experienced company. This partnership will drastically reduce the initial cost of building or acquiring the required knowledge.

Want to utilize AI with on-premise LLM deployment?

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