Custom LLM Development or Pre-built Solutions: What’s Right for Your Business?

Watch the presentation on this topic by AI expert Adam Czekalowski of Nvidia

‍

‍

The development of large language models (LLMs) has accelerated at an unprecedented pace. These sophisticated models, capable of understanding and generating human-like text, have revolutionised various industries, from customer service to content creation. As businesses consider integrating LLMs into their operations, they face a critical decision: should they create their own models, customise existing ones, or simply consume available services? This exploration covers the options, challenges, and considerations for businesses navigating the world of LLMs.

‍

The Evolution of Large Language Models

‍

Significant Milestones in LLM Development

The journey of LLMs began with the introduction of early models like GPT in 2018. These initial models could identify basic parts of speech and generate coherent text, marking a significant step forward in natural language processing. However, the capabilities of LLMs have grown exponentially since then.

By the end of 2020, GPT-3 emerged as a game-changer, boasting hundreds of times more parameters than its predecessors. This model introduced concepts like zero-shot and few-shot learning, where the model can perform tasks with little to no specific training data. The release of GPT-4 further enhanced these capabilities, demonstrating remarkable improvements in performance across various complex tasks.

Rapid Adoption and Industry Impact

The adoption of advanced LLMs like ChatGPT has been swift and impactful. Within just two months of its release, ChatGPT amassed over 100 million users, becoming the fastest-growing application in history. This rapid adoption highlights the immense potential and value LLMs bring to businesses, enabling them to automate and enhance a wide range of functions.

‍

Options for Utilising LLMs

‍

Consuming Pre-built LLM Services

For many businesses, the most straightforward approach to leveraging LLMs is through consumption of pre-built services. Numerous providers offer robust LLM services that can be integrated into existing workflows with minimal effort. This approach allows businesses to access advanced language capabilities without the need for extensive technical expertise or infrastructure investment.

While consuming LLM services is convenient, it comes with limitations. Customisation options are often restricted, meaning businesses might not be able to tailor the model precisely to their unique needs. However, for many applications, the available level of customization is sufficient to achieve significant improvements in efficiency and effectiveness.

Customising Existing Models

Businesses with specific requirements that cannot be met by off-the-shelf LLM services might consider customising existing models. This process involves tuning the model to better align with particular use cases, without altering the underlying architecture. Customisation techniques vary in complexity and resource demands, but generally fall into three main categories:

1. Fine-tuning: This method involves training the model on additional data specific to the business's domain. It is relatively resource-efficient and can yield significant performance improvements for targeted tasks.
2. Transfer learning: By leveraging knowledge from a pre-trained model, businesses can adapt LLMs to new tasks with limited data and compute resources. This approach is particularly useful for niche applications.
3. Reinforcement learning: More advanced customisation techniques, like reinforcement learning, involve optimising the model's behaviour based on feedback from its performance. This method requires substantial expertise and computational power but can produce highly specialised models.

Creating New LLMs from Scratch

For organisations with substantial resources and specific needs, building a new LLM from scratch might be the best option. This approach offers complete control over the model's architecture and training process, allowing for maximum customisation. However, it also demands significant investment in infrastructure, data curation, and technical expertise.

Creating an LLM from scratch involves several critical steps:

1. Data Collection and Curation: Assembling and preparing vast amounts of data is the foundation of training an effective LLM. This process is time-consuming and requires meticulous attention to detail to ensure data quality and relevance.
2. Infrastructure Setup: Establishing the necessary computing infrastructure is essential for training large models. This includes high-performance GPUs, storage solutions, and robust networking capabilities.
3. Model Training and Optimisation: The actual training process involves iteratively refining the model through extensive computation. Optimising the model to achieve the desired performance can take weeks or even months of continuous training.

Despite the challenges, creating a new LLM can yield a model precisely tailored to the organisation's needs, providing a competitive edge in applications requiring high specificity and performance.

‍

Critical Components for Successful LLM Projects

‍

Compute Infrastructure: The Backbone of LLM Success

A robust compute infrastructure is essential for the successful implementation of LLMs. The complexity and scale of these models require significant computational power, which can be provided through high-performance GPUs, CPUs, and DPUs. NVIDIA, a leader in accelerated computing, plays a crucial role in this space by offering cutting-edge hardware solutions.

Using NVIDIA as an example, NVIDIA's GPUs are renowned for their ability to handle the intensive processing demands of LLMs. More recently, the company has expanded its portfolio to include CPUs and DPUs, providing a comprehensive suite of computing solutions. These components are vital for both the training and inference stages of LLM deployment, ensuring that models can be developed and utilised efficiently.

Beyond the hardware, NVIDIA's software stack is equally important. About 75% of NVIDIA's engineering efforts are dedicated to software development, highlighting the critical role of optimised software in maximising hardware performance. This software stack covers the entire pipeline from data pre-processing to inference, enabling businesses to leverage the full potential of their computing resources.

The Importance of a Robust Software Stack

The software tools and frameworks used in LLM projects are just as crucial as the hardware. A well-designed software stack ensures that every stage of the LLM lifecycle is optimised for performance and efficiency. NVIDIA provides a comprehensive suite of software tools designed to support the entire LLM workflow.

For instance, NVIDIA's software accelerates data pre-processing tasks such as Spark and Pandas operations, often achieving performance improvements of up to five times. At the inference stage, tools like Triton Inference Server and TensorRT optimise models to deliver fast and efficient results. This end-to-end approach ensures that businesses can achieve significant performance gains, reducing both costs and environmental impact.

Leveraging AI Expertise

The third critical component for successful LLM projects is AI expertise. Developing, customising, and deploying LLMs requires specialised knowledge that many organisations might not possess in-house. Partnering with experts in the field can bridge this gap, providing the necessary guidance and support to navigate the complexities of LLM implementation.

NVIDIA collaborates with partner organisations like Deeper Insights to offer expert assistance to businesses. These partnerships ensure that organisations can make informed decisions about their LLM strategies, from choosing the right models to optimising performance and ensuring regulatory compliance.

‍

Addressing Challenges in LLM Implementation

‍

Data Curation and Training: The Foundation of Effective Models

Training effective LLMs requires vast amounts of high-quality data. The process of collecting and curating this data is both time-consuming and resource-intensive. Businesses must ensure that their data sets are comprehensive, relevant, and free from biases to achieve the best possible outcomes.

The infrastructure needed for training LLMs is another significant challenge. High-performance computing resources are essential, and the costs associated with this infrastructure can be substantial. Businesses must carefully consider their budgets and resource availability when planning LLM projects.

Utilising Open Source Models

For organisations looking to reduce costs and accelerate deployment, open-source LLMs offer a viable starting point. These models, available from various repositories, provide a foundation that can be further customised to meet specific business needs. However, there are limitations to this approach.

Open-source models often lack domain-specific knowledge, which can impact their performance in specialised applications. Additionally, these models are typically frozen in time, meaning they do not incorporate the latest data and trends. Businesses must find ways to update and enhance these models to ensure they remain relevant and accurate.

Implementing Guardrails for LLMs

To address the inherent challenges and risks associated with LLMs, businesses can implement guardrails—tools and strategies designed to ensure model outputs are appropriate and accurate. NVIDIA's guardrails framework offers three main functionalities to enhance model reliability:

1. Content Filtering: This feature uses predefined rules and examples to filter out inappropriate or irrelevant content. It ensures that the model's responses align with the desired guidelines and standards.
2. Knowledge Base Integration: By incorporating a vector database, guardrails can enhance the model's responses with additional relevant information. This approach improves the accuracy and relevance of the outputs, particularly for domain-specific applications.
3. Action Incorporation: Guardrails can integrate external actions, such as web lookups or database queries, to enrich the model's responses. This functionality ensures that the model provides comprehensive and fact-checked information.

‍

Practical Considerations for Businesses

‍

Budget and Resources: Strategic Investment

One of the most significant factors influencing the decision to create, customise, or consume LLMs is the budget. Developing a new LLM from scratch can be an enormous investment, often running into tens or even hundreds of millions of pounds. This high cost includes expenses for data acquisition, infrastructure, and the expertise needed to build and maintain the model.

For many businesses, especially those new to AI, starting with simpler, less resource-intensive options like consuming pre-built LLM services or customising existing models can be a prudent strategy. These approaches allow businesses to leverage the power of LLMs without the need for substantial upfront investment. As the business grows and the need for more tailored solutions arises, incremental investments in more advanced customisation or model creation can be considered.

Technical Knowledge and Expertise: Bridging the Gap

The successful implementation of LLMs requires specialised technical knowledge and expertise. This includes understanding AI and machine learning principles, as well as the specific intricacies of LLM development and deployment. Many businesses may not have this expertise in-house, which can be a significant barrier to LLM adoption.

To bridge this gap, businesses can partner with AI consulting firms or leverage the expertise of external organisations their partners. These collaborations provide valuable support, from initial planning and strategy to the technical execution of LLM projects. By tapping into external expertise, businesses can ensure they are making informed decisions and effectively navigating the complexities of LLM implementation.

Regulatory and Business Use Considerations: Ensuring Compliance and Alignment

Regulatory compliance is a critical aspect of integrating LLMs into business operations. Different industries have varying regulations governing the use of AI, particularly concerning data privacy and ethical considerations. Businesses must stay informed about these regulations and ensure their LLM implementations comply with all relevant legal standards.

Additionally, the intended business use of the LLM should drive the strategy for its implementation. For applications with a broad user base or significant impact, a higher investment in customisation and fine-tuning may be justified. Conversely, for smaller-scale applications, more straightforward solutions like consuming pre-built services may be sufficient. Aligning the LLM strategy with the business's specific goals and user needs ensures that investments are both efficient and effective.

‍

Strategies for LLM Integration

‍

Starting with Simple Customisations

For businesses new to LLMs, starting with simple customisations of existing models is a practical approach. This strategy allows businesses to quickly deploy LLM capabilities tailored to their specific needs without the complexity and cost of developing a new model from scratch.

Simple customisations can include fine-tuning pre-trained models with domain-specific data. This process enhances the model's relevance and accuracy for particular applications while keeping resource requirements manageable. Businesses can experiment with various customization techniques to find the optimal balance between performance and cost.

Scaling Up with Advanced Customisations

As businesses become more comfortable with LLMs and their specific needs become more apparent, scaling up with advanced customisations is the next logical step. Techniques such as reinforcement learning and transfer learning allow for more sophisticated model adjustments, resulting in higher performance and greater alignment with business objectives.

Advanced customisations involve a higher level of expertise and computational resources. However, the benefits can be substantial, including improved model accuracy, efficiency, and the ability to handle more complex tasks. By gradually scaling up their customisation efforts, businesses can incrementally enhance their LLM capabilities while managing costs and risks.

Creating Custom Models: The Ultimate Tailoring

For businesses with unique needs and substantial resources, creating custom LLMs from scratch offers the highest level of tailoring. This approach provides complete control over the model's architecture and training process, ensuring it is perfectly aligned with the business's specific requirements.

The process of creating custom models is resource-intensive, involving extensive data collection, infrastructure setup, and iterative training. However, the resulting model can provide unparalleled performance and specificity, offering a significant competitive advantage in applications that demand high precision and customisation.

Hybrid Approaches: Balancing Cost and Performance

Many businesses may find that a hybrid approach, combining elements of consumption, customisation, and creation, provides the best balance of cost and performance. For example, a business might start with consuming a pre-built service, move to simple customisations as their needs evolve, and eventually develop custom models for highly specialised applications.

This flexible strategy allows businesses to adapt their LLM implementations to their changing needs and resources. By leveraging the strengths of each approach, businesses can optimise their LLM investments and achieve the best possible outcomes.

‍

Final Thoughts

Integrating LLMs into business operations offers significant potential for enhancing efficiency, productivity, and innovation. However, the decision to create, customise, or consume LLMs requires careful consideration of factors such as budget, technical expertise, regulatory compliance, and specific business needs.

By understanding the critical components of successful LLM projects and addressing the associated challenges, businesses can make informed decisions that align with their goals and capabilities. Whether starting with simple customisations, scaling up with advanced techniques, or creating custom models from scratch, businesses can harness the power of LLMs to drive growth and achieve their strategic objectives.

For those seeking guidance and support, partnering with experienced organisations and their network of experts can provide valuable insights and assistance. With the right strategy and resources, businesses can successfully navigate the complexities of LLM integration and unlock the full potential of AI in their operations.