Enterprise AI Competition: Is Success for SLMs more Likely than LLMs in the Business Arena?
Abhi Maheshwari is the head of Aisera Inc., a company specializing in agentic AI.
Large language models (LLMs) have been taking center stage recently, as they become increasingly intricate. The number of parameters these models use keeps rising, with OpenAI's upcoming GPT-5 rumored to surpass a mind-boggling trillion parameters.
On the other hand, SLMs, or smaller counterparts of LLMs, have significantly fewer parameters, usually ranging from a few million to a few billion. Major tech companies have been pouring significant resources into developing these SLMs. Meta has introduced compact models within its Llama series, while OpenAI is working on its o1-mini. Microsoft is pushing forward with its phi-3 family, and Google is focusing on Gemma.
But which model is better for enterprise apps—SLMs or LLMs? As someone who leads an AI company, Maheshwari has had the chance to test various SLMs and LLMs. The results have shown that, despite their smaller size, SLMs have delivered similar or even better results in some instances compared to larger models. In an era where more is often equated with better, it's becoming clear that less can often be more powerful.
Problems with LLMs
LLMs showcase an array of impressive abilities, making them suitable for complex tasks. However, they are trained on diverse datasets, which can result in a lack of customization for specific enterprise needs, such as domains. According to an article in Dataconomy, "This generality may lead to gaps in handling domain and industry-specific nuances, potentially decreasing the effectiveness of their responses."
Despite their strengths, LLMs also present some significant challenges. The most significant issue is "hallucinations"—instances where the LLM generates content that looks accurate but turns out to be factually wrong or nonsensical. Another challenge is the high cost and time overhead. Training LLMs requires substantial computational resources (hundreds of GPUs/TPUs) and can take months. The average training costs for a top-tier LLM like GPT-4 are in the millions.
As LLMs are trained on vast datasets, these models can also be influenced by biases present in the data used to train them, leading to poor performance in specialized domains.
Furthermore, LLMs present maintenance challenges for enterprises. Retraining LLMs to update their knowledge is a slow and expensive process, making them impractical for environments that need to adapt rapidly to new data or compliance changes. Customizing LLMs for different industries requires substantial fine-tuning efforts, pushing back the time-to-market.
As a result, the accuracy, time, cost, and lack of agility can make LLMs less appealing to enterprise applications. Instead of driving innovation, they often become roadblocks, hindering the swift deployment of tailored AI solutions.
The Strength of SLMs
On the other hand, SLMs are specifically designed to excel in specific tasks, making them more accessible for organizations with limited resources and easier to fine-tune to meet specific needs.
Maheshwari finds that SLMs can be trained in a fraction of the time and with a mere fraction of the resources LLMs require. By reducing hardware and training costs, SLMs drastically reduce the cost of ownership, enabling enterprises to deploy AI models for diverse needs without breaking the bank. In fact, Maheshwari's experience has shown that SLMs cost just 1/10th of what LLMs require, providing a highly cost-effective solution for many enterprise applications.
Domain-specific SLMs, developed with specialized data, can help ensure greater accuracy and minimize errors, outperforming models that rely on generic data for specialized tasks. Moreover, SLMs can be seamlessly customized for individual business units, departments, or industry verticals. Their smaller footprint allows for faster and more efficient adaptation to unique operational requirements.
Going a step further, when SLMs are integrated with company-specific knowledge graphs, they can provide richer context, allowing them to understand enterprise-specific terms and adapt to evolving data. As a result, they can enrich queries with near-zero latency.
Additionally, SLMs can be updated more frequently, keeping them in line with changing domain needs or regulations. Their ability to adapt quickly to individual customer needs offers a balance between efficiency and precision.
As a result, companies using SLMs can react more swiftly to market shifts and customer needs, giving them a competitive edge over those hampered by the rigidity of LLMs. This cost-effective flexibility and adaptability can ultimately help reduce the total cost of ownership (TCO) while increasing ROI.
Limitations
It's essential to recognize that deploying SLMs comes with its trade-offs. Due to their training on smaller datasets, SLMs possess more limited knowledge bases compared to LLMs' extensive knowledge.
Therefore, Maheshwari recommends using SLMs for specific tasks, such as summarizing context or answer generation, content tagging, and so on. These are areas where their specialized focus and efficiency offer significant benefits.
One Size Does Not Fit All
Enterprises aren't simply moving away from large models. LLMs remain the go-to option for solving many complex tasks. Integrating LLMs might still be necessary for tasks requiring extensive general knowledge or deep contextual understanding. Instead, enterprises are adopting a diverse portfolio of models, allowing them to select the best one for each scenario.
Choosing the right language model depends on an organization’s specific needs, the complexity of the task, and available resources. Organizations leveraging multiple models or looking to leverage multiple models will need an LLM gateway to connect the appropriate LLMs based on understanding, context, or domain.
The surge of compact language models indicates a significant change in AI's industry application. Although these models show remarkable abilities, they also present substantial difficulties.
In contrast, scalable language models (SLMs) can provide a smoother, budget-friendly alternative, particularly when paired with LLMs. Additionally, SLMs are simpler to modify and adapt to exclusive data settings.
In summary, SLMs demonstrate that size isn't everything. I believe they are poised to become a favorite choice for numerous businesses.
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- Key Takeaways
- Compact language models show potential as an industry-wide solution.
- SLMs may offer cost-effectiveness and customization advantages.
- LLMs and SLMs could complement each other's strengths.
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emphasis added
Abhi Maheshwari, the head of Aisera Inc., is also a key advocate for the use of SLMs in enterprise applications due to their cost-effectiveness and ability to deliver similar or better results compared to larger models in some instances. Despite LLMs' impressive abilities, the high cost, time overhead, and challenges in customization and updating make them less appealing for enterprise applications, leading organizations to seek alternatives like SLMs.
