Forget giant, expensive AI models! Small Language Models (SLM’s) are here to offer businesses a powerful, affordable, and secure solution. Perfect for on-device applications and data-sensitive industries, SLM’s can be your secret weapon. Dive in to see how they can address your challenges and save you money!
Introduction
Generative AI exploded onto the scene in late 2022 with the release of ChatGPT, powered by OpenAI’s GPT-3.5 Large Language Model. Since then, the language model landscape has become a bustling marketplace, with new offerings constantly emerging. While some models generate headlines with their impressive feats, others quietly fade into the background.
A dominant trend in this space has been the rise of large, pay-as-you-go, API-based models. These models excel at complex tasks – orchestrating workflows, analyzing vast datasets, and understanding intricate context. However, not all businesses require such a powerhouse solution.
For some businesses, the ideal scenario involves building applications that run locally on devices. Such tasks may not demand extensive reasoning. Additionally, regulated industries and sectors often face situations where they crave high-quality results but must keep data strictly on-premises.
This is where Small Language Models (SLM’s) enter the picture, offering a compelling alternative. This blog post will delve into how SLM’s can effectively address business challenges, prove particularly valuable for regulated industries, and deliver significant cost savings.
What are Small Language Models (SLM’s)
Traditionally, Small Language Models (SLMs) were defined by their relatively modest size, typically boasting a few million parameters. However, the relentless pace of AI research has re-drawn the battle lines. So much so that in December 2023, when Microsoft unveiled Phi-2, a model with 2.7 billion parameters – they introduced it as an SLM. This highlights a fascinating shift in the field: the very definition of “small” is being redefined.
Note: In layman terms, parameters is a unit of measure that refers to the algorithmic knobs on a model that help determine its output.
A Brief History of Small Language Models
Some of the more impressive small language models till date where the parameter sizes are publicly disclosed.
Business Applicability
Small Language Models (SLM’s) are undergoing a remarkable transformation. Traditionally seen as less capable cousins to their larger counterparts, SLM’s are now exhibiting impressive growth in core capabilities like general AI, commonsense reasoning in natural language, multitask language understanding, decision-making, solving hard problems, step-by-step reasoning, code generation, etc. These strengths are measured through a series of benchmark tests, some of the most prominent being AGI Eval, MMLU, BigBench Hard, ANLI, HellaSwag, PIQA, GSM8K, HumanEval, etc.
For much of 2023, only large, pay-as-you-go API-based models reigned supreme on these benchmarks. However, fueled by the surge in business interest in AI, research in SLM’s has accelerated dramatically. SLM’s are now routinely surpassing their larger counterparts on many of these benchmarks.
This shift signifies a significant development in the AI landscape. Businesses can now leverage the power of AI through models that are not only effective but also potentially deployable on-premises, a critical factor for data privacy and security in regulated industries. Coupled with the cost-saving advantages, SLM’s can empower businesses of all sizes!
Exhibit 1: Phi-3 published benchmark results (ref: https://azure.microsoft.com/en-us/blog/introducing-phi-3-redefining-whats-possible-with-slms/)
Exhibit 2: Llama-3 (8B) published benchmark results (ref: https://ai.meta.com/blog/meta-llama-3/)
Data Privacy and Security
SLM’s deployed locally can process data on-premises, ensuring that sensitive customer information, proprietary business data, and confidential communications remain within the organization’s control. This significantly reduces the risk of data breaches and unauthorized access, which is critical for compliance with regulations like GDPR in Europe and HIPAA in the United States.
For example, consider a healthcare provider using language models to analyze patient data. With a Small Language Model, patient information is processed and stored within the healthcare provider’s secure infrastructure, minimizing exposure to potential cyber threats. This approach is crucial for maintaining patient confidentiality and complying with HIPAA regulations.
In contrast, API-based LLM’s send data to external servers for processing, introducing several vulnerabilities. Data can be intercepted during transmission, and unauthorized access on the external servers is a risk. While these providers implement robust security measures, the inherent risk of data exposure is higher compared to local processing.
Financial institutions dealing with sensitive financial transactions and personal data also benefit from Small Language Models. By keeping data in-house, banks and financial firms can better protect against data breaches and fraud, ensuring customer trust and regulatory compliance.
Cost Efficiency
When evaluating the cost efficiency of Small Language Models versus API-based Large Language Models, the financial implications can be substantial, especially for businesses with heavy usage and long-term needs.
SLM’s shine in scenarios where businesses require frequent, high-volume processing. By running models on-premises, companies eliminate the recurring costs associated with API usage, which can escalate quickly with extensive use.
For instance, a marketing firm utilising language models to generate content and analyse consumer sentiment can significantly reduce costs by hosting the models locally. The initial investment in hardware and setup is offset by the savings accrued from not paying per-request or subscription fees to an API provider.
Moreover, SLM’s offer cost-effective scalability. Businesses can scale their computational resources as needed, avoiding the unpredictable costs associated with fluctuating API usage.
However, API-based LLM’s have their own cost advantages, particularly for smaller businesses or those with infrequent use cases. These businesses can avoid the upfront investment in hardware and ongoing maintenance costs associated with local deployments.
For instance, a small e-commerce site using a language model for occasional product recommendations would find API-based models more cost-effective, as they only pay for what they use, without the burden of managing complex infrastructure.
Additionally, API-based models provide access to the latest advancements and updates without additional costs or technical expertise. Large enterprises might prefer APIs for rapid prototyping or for use cases requiring cutting-edge models that are continuously updated by providers, thereby leveraging state-of-the-art capabilities without direct investment in R&D.
Challenges and Considerations for Evaluating the Adoption of SLM’s versus API-Based LLM’s
When evaluating the adoption of Small Language Models versus Large Language Models, organisations must carefully weigh several challenges and considerations. These factors are crucial for business executives to make informed decisions tailored to their specific needs and constraints.
- Technical Expertise and Resources
| Small Language Models | Large Language Models |
| Requires significant technical expertise for setup, optimization, and maintenance. | Lower technical barrier to entry, as the provider handles maintenance and updates. |
| Businesses need a skilled team proficient in machine learning and IT infrastructure. | Suitable for organizations without extensive in-house technical expertise. |
- Hardware and Infrastructure
| Small Language Models | Large Language Models |
| Requires investment in computational resources such as GPUs, storage, and servers. | No need for substantial hardware investment, but costs accumulate with usage. |
| Initial capital expenditure can be high, but operational costs might be lower over time. | Pay-as-you-go model can be cost-effective for intermittent usage but expensive for high-frequency tasks. |
- Data Privacy and Security
| Small Language Models | Large Language Models |
| Requires investment in computational resources such as GPUs, storage, and servers. | Data is processed on external servers, raising concerns about data security and compliance. |
| Initial capital expenditure can be high, but operational costs might be lower over time. | Suitable for less sensitive data or when providers offer robust security guarantees. |
- Latency and Performance
| Small Language Models | Large Language Models |
| Offers lower latency and better performance, especially in environments with poor connectivity. | Dependent on network connectivity, which can introduce latency. |
| Ideal for real-time applications requiring immediate responses. | May be acceptable for applications where slight delays are tolerable. |
- Customization and Flexibility
| Small Language Models | Large Language Models |
| Allows for extensive customization to meet specific business needs and workflows. | Limited customization options, as models are typically generic to serve a wide user base. |
| Businesses can tailor models to their unique requirements and maintain control over updates. | Suitable for standard tasks where customization is less critical. |
Future Outlook for Small Language Models in Business Applications
The future of Small Language Models in business applications is promising, driven by their inherent advantages and evolving technological landscape. As organisations continue to seek ways to enhance operational efficiency, data security, and cost management, Small Language Models are poised to become a cornerstone in various industries.
1. Enhanced Data Privacy and Security
Small Language Models will continue to be the go-to solution for businesses prioritising data privacy and regulatory compliance. The increasing number of data breaches and stringent regulations like GDPR and HIPAA underscore the need for secure, on-premises data processing. Companies handling sensitive information, such as healthcare providers and financial institutions, will increasingly adopt Small Language Models to safeguard their data and maintain customer trust.
2. Cost Efficiency and Scalability
The financial benefits of locally hosted models are significant, especially for organisations with high-frequency, large-scale applications. By reducing dependency on costly API calls, businesses can achieve long-term cost savings and scalability. As hardware costs decrease and computing power becomes more accessible, even small to medium-sized enterprises will find local deployments more financially viable.
3. Superior Latency and Performance
Small Language Models offer lower latency and superior performance, essential for real-time applications. Industries like manufacturing, logistics, and retail will leverage these models for predictive maintenance, real-time customer interactions, and supply chain optimization. The ability to process data quickly and efficiently on-site will drive innovation and improve operational responsiveness.
4. Customization and Flexibility
The future will see a rise in highly customised language models tailored to specific business needs. Locally hosted models provide the flexibility to be optimised for unique workflows, enabling businesses to differentiate themselves through bespoke solutions. This adaptability will be a key driver for sectors requiring specialised applications, such as legal tech and personalised marketing.
5. Overcoming Challenges with Advanced Tools
While technical expertise and infrastructure requirements have been hurdles, advancements in deployment tools and platforms are simplifying the process. Services like llama.cpp, Ollama, gpt4all, NVIDIA NIM, etc. are offering user-friendly interfaces and robust support, making locally hosted models accessible even to organisations with limited technical resources. These developments will lower barriers to entry and democratise access to advanced language models.
6. Integration with Emerging Technologies
Small Language Models will increasingly integrate with emerging technologies such as edge computing and the Internet of Things (IoT). This synergy will enable businesses to harness real-time data from connected devices, further enhancing decision-making and operational efficiency. The convergence of AI and IoT will open new avenues for innovation in smart cities, autonomous systems, and beyond.
Key Takeaway!
The future of Small Language Models in business applications is bright and full of potential. Their advantages in data privacy, cost efficiency, performance, and customization position them as a vital tool for forward-thinking organisations. As technology continues to evolve, the barriers to implementing Small Language Models will diminish, enabling more businesses to reap the benefits. However, the future of AI is not a binary choice between large and small models. Industry experts predict a move away from a singular category, ushering in an era of diverse “model portfolios.” This empowers businesses to select the most suitable tool for each specific scenario. This flexibility allows businesses to tailor their AI solutions with precision, ensuring optimal performance and cost-effectiveness.
By staying informed and adapting to these trends, executives can ensure their organisations remain competitive and innovative in an increasingly data-driven world. Stay tuned for more!
Disclaimer: The views and opinions expressed in this blog are solely those of the author and do not necessarily represent the views or positions of the author’s employer.
SLM is the best way to start implementing GET AI in the organization. The SLM modules like Llama , XGen are easy to implement and have great result.
Sanket ji thank you so much for sharing this great article and for sure this will help us to add great value to our work.
Thanks again.
Quite interesting to see the pragmatic shift of definition of small data models with capability of handling billions of data sets.
SLMs are practical and feasible options for all kind of tech companies and may be a tad bit more relevant to small to mid size companies.
It offers a first hand exp at leveraging the use of these models to solve various business problems and in turn provide solutions, improve efficiency and productivity with tangible benefit of cost savings in the long run. Thanks for the write up Sanket !!!