SE Radio 661: Sunil Mallya on Small Language Models
Mar 25, 2025
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Sunil Mallya, Co-founder and CTO of Flip AI, shares his expertise on small language models (SLMs) versus large language models (LLMs). He delves into their differences, revealing how SLMs can be more efficient and accurate for specific tasks. Sunil highlights the importance of domain-specific training datasets and discusses recent advancements like the DeepSeek R1 that show smaller models outperforming larger ones in particular contexts. He also touches on the evolving landscape of model deployment and how organizations can optimize performance while managing costs.
Small language models (SLMs) prioritize specialization and efficiency, making them preferable for niche applications over larger language models (LLMs).
Training SLMs effectively requires high-quality domain-specific data, which can yield accurate results with smaller datasets compared to LLMs.
Innovations in inference techniques and deployment frameworks are enhancing the adaptability and effectiveness of SLMs for enterprise solutions.
Deep dives
Understanding Small Language Models (SLMs)
Small language models (SLMs) are defined not solely by their size but by their practicality and resource requirements. These models can operate effectively without the extensive GPU resources that larger models, like large language models (LLMs), typically demand. As of early 2025, a 10 billion parameter model with a maximum context length of 10,000 words and a one-second inference latency exemplifies what constitutes an SLM. The definition of what is considered 'large' evolves over time, influenced by advancements in underlying hardware and the rapid development of AI models.
Expert vs. General-Purpose Models
SLMs often emphasize specialization, creating expert models that excel in specific domains compared to general-purpose LLMs, which are trained on extensive and diverse datasets. While LLMs can handle varied topics due to their broad training, expert models focus deeply on a limited subject matter, enhancing their precision and performance in that area. This trade-off leads to SLMs being more suitable for niche tasks, enabling developers to create models that are expert enough for precise applications, while LLMs may underperform in specialized cases. The variance in model focus impacts the output and accuracy, as SLMs draw on specific data relevant to their training domain.
The Importance of Data Curation
Effective training of SLMs relies significantly on the quality and suitability of training and evaluation data. SLMs can achieve accurate results using considerably smaller datasets than LLMs, which require vast amounts of diverse training data to maintain accuracy. Proper curation and testing are critical; if the training data do not reflect real-world scenarios, the model's accuracy can drop significantly, leading to performance discrepancies once deployed. This emphasizes the need for rigorous data oversight to ensure that both training and evaluation datasets are representative of the intended application context.
Computational Footprint and Efficiency
The computational demands of SLMs are generally lower than those of LLMs, which enables faster processing times and reduced costs for deployment. As model size typically correlates with computational footprint, an SLM will require fewer resources, making it more practical for enterprises with limited infrastructure. Additionally, architectural advancements such as mixture of experts allow for efficient model activation, enabling even greater optimization. Understanding these aspects is crucial for developers to find the right balance between accuracy, performance, and budget in their AI applications.
Emerging Research and Future Directions
One exciting trend in AI research involves advancing inference techniques, like speculative decoding, which enhances response times by predicting multiple tokens simultaneously. Innovations in deployment frameworks, particularly on-device capabilities and privacy-aware methodologies are gaining traction, making SLMs increasingly accessible for varied applications. Furthermore, using reinforcement learning to directly train models from scratch represents a groundbreaking shift in model development methods. These advancements promise to reshape the landscape of AI, making SLMs more effective and flexible for specific use cases and enterprise environments.
Sunil Mallya, co-founder and CTO of Flip AI, discusses small language models with host Brijesh Ammanath. They begin by considering the technical distinctions between SLMs and large language models.
LLMs excel in generating complex outputs across various natural language processing tasks, leveraging extensive training datasets on with massive GPU clusters. However, this capability comes with high computational costs and concerns about efficiency, particularly in applications that are specific to a given enterprise. To address this, many enterprises are turning to SLMs, fine-tuned on domain-specific datasets. The lower computational requirements and memory usage make SLMs suitable for real-time applications. By focusing on specific domains, SLMs can achieve greater accuracy and relevance aligned with specialized terminologies.
The selection of SLMs depends on specific application requirements. Additional influencing factors include the availability of training data, implementation complexity, and adaptability to changing information, allowing organizations to align their choices with operational needs and constraints.
