By: Tom Christensen, Head of Thought Leadership - Nordics, LTIMindtree

Discussions about Gen AI apps often turn to values such as efficiency, growth, and streamlining costs for businesses. However, it is equally important to spend time on the technology behind and the impact of the different language models that exist today. The artificial intelligence landscape has evolved significantly with the introduction of Small Language Models (SLMs) and Large Language Models (LLMs). These advanced AI systems are reshaping industries and redefining business operations. In this blog post, we will explore various types of LLMs and discuss key considerations to keep in mind.

LLMs revolutionizing business operations

LLMs are advanced AI systems designed to understand human language and to generate intelligent and creative responses when queried. Successful LLMs are trained on enormous datasets, typically measured in petabytes. Training data is often sourced from books, articles, websites, and other text-based sources. Using deep learning techniques, these models excel at understanding and generating text similar to human-produced content.

The debate: Small or large language models?

In any language model, the parameters are variables that can be trained. More parameters mean more knowledge in the model, but bigger isn’t always better. Smaller LLM models with fewer parameters require less computing power, making them quicker to fine-tune and deploy, especially for specific tasks at a lower cost. On the other hand, larger models with typically over 10 billion parameters can learn and offer more accurate and contextually relevant results. However, they demand more infrastructure, training, and customization resources.

A comparison is listed here:

The decision between small and large models is not just about cost and performance but also the capability to process data locally. This proves beneficial for enterprises governed by strict privacy and security regulations. Not to forget the ethical aspect, which is gaining momentum on a global scale, as seen in initiatives like the EU AI Act.

Comparing SLMs to LLMs

Let us look under the hood to compare costs, performance, privacy, security, and ethical considerations. This provides insights into the distinctions between small and large models.

• Cost: SLMs generally have lower computational requirements than LLMs, making them more cost-effective in terms of training and deployment. LLMs, being larger and more complex, can incur higher processing and maintenance costs.
• Performance: LLMs typically exhibit superior performance in handling complex language tasks due to their larger capacity and training data. On the other hand, SLMs may suffice for simpler tasks but could lack the performance capabilities of LLMs for more advanced applications.
• Privacy: SLMs, especially those that process data locally, offer enhanced privacy as they reduce the need to transfer sensitive data to external servers. This can be crucial for industries with strict privacy regulations. LLMs may pose privacy concerns if they require extensive data sharing or processing on external platforms. Here, there is a need to seek advice on designing a secure solution.
• Security: Regarding security, SLMs that keep data localized may provide better security measures by minimizing data exposure risks. LLMs, especially when dealing with vast amounts of data, might face challenges ensuring data security and protection against potential breaches.
• Ethical considerations: Ethically, businesses need to evaluate the implications of using SLMs versus LLMs, considering factors like bias mitigation, fairness, transparency, and responsible AI deployment. LLMs, being more complex, may require additional scrutiny to ensure ethically sound use in AI applications.

Considering these factors, businesses must weigh the trade-offs between cost, performance, privacy,  security, and ethical standards when deciding between SLMs and LLMs for their specific Gen AI apps.

Cost aspect

The pricing for running GenAI on SLMs versus LLMs can vary significantly depending on various factors. These factors include token usage, model complexity, and the specific processing requirements of the task at hand. Understanding these nuances is crucial for optimizing cost efficiency and performance when utilizing different model sizes in GenAI applications.

• For SLMs, a pricing structure may involve a cost per token processed or a flat fee for utilizing the model within certain limits. This could result in lower overall costs for businesses with moderate processing needs.
• On the other hand, LLMs typically come with higher pricing due to their advanced capabilities and larger computational requirements. Pricing for LLMs may be based on factors like model size, training time, and the volume of data processed. Businesses requiring extensive language processing tasks or complex analyses may need to budget for higher expenses when utilizing LLMs.

This website gives access to an OpenAI GPT Pricing calculator that displays the cost per query for various GPT models using LLM.

Ultimately, the pricing example for running Gen AI apps on SLMs versus LLMs will depend on the specific requirements of the task, the scale of operations, and the desired level of performance and accuracy needed for the application.

Diverse Forms of LLMs

LLMs come in diverse types, each tailored to specific tasks and applications. These variations in LLMs cater to different needs, from complex natural language understanding to advanced text generation. The flexibility of LLMs allows for customization based on the unique requirements of various projects and industries. This enhances their adaptability and effectiveness in serving a wide range of purposes.

For instance, the best-known example is ChatGPT, which uses LLMs that offer search and text generation across multiple languages, and multimedia content based on public data. This is a generally available model offering services across many sectors. Another example is BloombergGPT, a purpose-built model that uses LLMs tailored and trained for the financial sector, enhancing tasks like financial analysis and customer service. The model pre-selects data from both public and private sources and outperforms existing models on financial tasks by significant margins. It also maintains competitive performance on general LLM benchmarks.

Framework for hosting language models

As we move forward, it becomes important to establish a framework with access to public language models and design your own. One of the latest innovations we have witnessed in the market is Cortex from Snowflake. This provides access to LLMs trained by experts at Mistral, Reka, Meta, and Google, including their own open enterprise-grade model. Since Snowflake fully hosts and manages these LLMs, data remains within the platform, delivering the performance, scalability, privacy, and governance standards you require.

Conclusion

The rise of large language models marks a significant milestone in the evolution of AI. By integrating the right LLMs into their operations, businesses can unlock unprecedented efficiency, enhance decision-making, and stay competitive in dynamic markets. However, prudent AI adoption, addressing ethical considerations, and ensuring responsible use are essential for leveraging the full potential of LLMs in business contexts.

Upon choosing your LLM or developing a custom one, you gain the ability to craft AI applications. Nonetheless, as emphasized in the blog, it is crucial to factor in certain considerations during the development process. These insights can significantly impact the effectiveness and success of your AI initiatives.

References

Charting New Horizons: The Impact of the EU AI Act on Business, Tom Christensen, LTIMindtree, June 18, 2024: https://www.ltimindtree.com/blogs/charting-new-horizons-the-impact-of-the-eu-ai-act-on-business/

OpenAI GPT API Pricing Calculator, GPT for Work: https://gptforwork.com/tools/openai-chatgpt-api-pricing-calculator

Introducing BloombergGPT, Bloomberg’s 50-billion parameter large language model, purpose-built from scratch for finance, Bloomberg, March 30, 2023: https://www.bloomberg.com/company/press/bloomberggpt-50-billion-parameter-llm-tuned-finance/

BloombergGPT: How We Built a 50 Billion Parameter Financial Language Model, Toronto Machine Learning Series (TMLS), https://www.youtube.com/watch?v=m2Scj2SO85Y

Snowflake Cortex AI, https://www.snowflake.com/en/data-cloud/cortex/