India’s IT Giants Roll Out Small Language Models to Transform Services
Micro and Small Language Models Set to Revolutionize Cost Efficiency for Enterprises
India’s leading software service providers, including Infosys, HCLTech, and Tech Mahindra, are now focused on developing micro and small language models (MLMs and SLMs). These innovations aim to reduce operational costs while customizing services and solutions across various sectors such as banking, cybersecurity, and telecommunications.
Experts suggest that these specialized models, optimized for specific functions, are providing faster responses at lower costs. This creates a favorable scenario for enterprises and software service providers, particularly for low-to-medium complexity applications. The models are expected to deliver similar, if not superior, quality compared to their larger counterparts.
According to IT executives, this trend presents a compelling business case with reduced investment requirements. “In Generative AI, we have built four small language models for banking, IT operations, cybersecurity, and more broadly for enterprises,” stated Salil Parekh, CEO of Infosys, India’s second-largest IT firm.
C. Vijayakumar, CEO of HCLTech, emphasized the significance of cost-effectiveness. “The real differentiator is that small models, like the ones used by Agentic, lead to a more compelling price-performance dynamic for our objectives,” he remarked.
Vijayakumar predicts that the adoption of SLMs will gain momentum, owing to increased interest driven by legacy modernization programs. “Currently, I see this trend as a net positive,” he noted.
Transforming Regulated Industries
Sectors burdened with regulatory and compliance challenges, such as banking and telecom, stand to benefit significantly from these advancements. As these industries embark on digital transformations, they are becoming prime candidates for employing these innovative models.
Large language models (LLMs) are equipped to comprehend and generate human-like text by analyzing extensive amounts of data. However, adoption rates have been hampered by concerns regarding costs and data accuracy.
A key distinction exists between LLMs and SLMs or MLMs. While LLMs can be biased due to their reliance on general public data, SLMs are specifically trained using curated data tailored to unique requirements, leading to improved accuracy and relevance.
The Need for Optimized Solutions
As the prices of requisite hardware are projected to rise throughout 2024, the demand for optimized solutions catering to specific use cases becomes increasingly critical. “This shift is fueling IT service providers’ interest in SLMs, as they aim to deliver focused, intelligent solutions while enhancing efficiency and cost-effectiveness,” explained Abhigyan Malik, Practice Director at technology consultancy Everest Group.
Vijayakumar noted that the cost of utilizing LLMs and conversational AI models has fallen by more than 85% since early 2023, further encouraging their adoption.
Meanwhile, Tech Mahindra is shifting its focus from LLMs to producing SLMs and tiny LMs. “Customers find tremendous value in these models for niche applications, allowing them to address specific challenges without exhausting too much computational power or carbon resources,” said CEO Mohit Joshi.
Building Small Language Models
According to Parekh, SLMs are constructed using proprietary data that companies possess, establishing a foundation based on industry standards or horizontal data followed by client-specific integrations. “This is genuine Generative AI work, enabling us to assist clients in creating their own small language models,” he mentioned during an earnings announcement event.
Industry analysts indicate that IT service providers are employing a blend of LLM adaptations and open-source technologies to develop customized solutions tailored to client needs. The focus remains on specific segments, positioning these advancements as vital themes for the future of AI technologies, alongside GPUs and AI PCs.
Conclusion
As India’s IT giants embrace SLMs and MLMs, they pave the way for enhanced efficiency and effectiveness in service delivery. With a focus on cost reduction and precise functionality, these models are set to transform operations across various sectors, driving the next wave of AI adoption in the enterprise landscape.
FAQs
- What are micro and small language models?
Micro and small language models (MLMs and SLMs) are specialized AI models designed to provide faster responses and lower costs for specific applications in industries like banking and telecommunications.
- Why are Indian IT service providers focusing on SLMs?
Providers are focusing on SLMs to achieve cost-efficiency and to meet the growing demand for tailored solutions across regulatory sectors impacted by digital transformation.
- What distinguishes SLMs from LLMs?
SLMs are trained using specific, curated datasets tailored to particular domains, thus improving accuracy and reducing bias compared to larger language models (LLMs), which may draw on generalized public data.
- How are IT firms building these models?
IT firms develop SLMs by utilizing a combination of proprietary data, standard industry data, and adapting larger models or open-source technologies to fit client specifications.
- What impact do SLMs have on AI adoption in enterprises?
By offering a more cost-effective and efficient way to implement AI solutions, SLMs are expected to accelerate AI adoption among enterprises, leading to more innovation in various sectors.
