Small Language Models (SLMs) are highly specialized models with leaner operational requirements and few limitations than Large Language Models (LLMs). Discover how SLMs can deliver better business outcomes for businesses.
There’s no denying that large language models (LLMs) have pushed the boundaries of what’s possible in AI. Using hundreds of billions of parameters, these models can generate impressive text, solve complex reasoning tasks, and demonstrate remarkable versatility across domains.
But there’s a catch: most enterprises don’t have the budget to support LLM-based agents and applications at scale. Even with recent efficiency gains, the operational requirements of running optimized LLMs in production remain substantial. And hyperscalers know it, requiring businesses to buy capacity blocks on costly newer chips (example: a minimum purchase for H100 requiring an 8-GPU machine configuration).
But there’s an alternative: small language models (SLMs). These highly specialized models have leaner operational requirements—and fewer limitations—than their LLM counterparts. They’re also significantly less expensive to maintain—as much as 100x less. That’s game changing for organizations that handle tens (or hundreds) of thousands of interactions regularly.
In our recent whitepaper, “The Small Model Revolution: How SLMs with RAFT Are Transforming Business AI,” we explore how SLMs are addressing the shortcomings of LLMs and enabling businesses to deploy faster, more accurate AI at scale—and at a fraction of the cost. Read on for a brief overview or download the full paper below.
The Small Model Revolution: How SLMs with RAFT Are Transforming Business AI
SLMs are fulfilling the promise made by LLMs
While businesses today can (and do) leverage broad LLMs for a number of enterprise-specific purposes, the shortcomings of these models are too big to ignore. Sky-high infrastructure and operational costs, latency concerns, and hallucination risks significantly limit their viability as enterprise-scale AI models.
That’s because, despite creative workarounds (such as quantization, pruning, and other optimization techniques), LLMs are fundamentally “generalist” by design. They will never deliver the “specialist” precision required by enterprise agentic systems—or promised by hyperscalers. Their architecture simply won’t allow it.
By contrast, small language models (SLMs) represent a different architectural philosophy: depth over breadth, precision over generalization. Instead of trying to know everything about everything, SLMs excel at knowing everything about something specific. In an enterprise setting—where specialized tasks are the norm—these models deliver where LLMs fall short.
Taking SLMs from specialized to domain-specific with RAFT
If you’re familiar with LLMs, no doubt you’ve heard of retrieval augmented generation (RAG). RAG strengthens the accuracy and contextual relevancy of LLMs by supplementing them with internal enterprise data sources. However, because it requires real-time retrieval for every query, RAG adds latency and introduces multiple failure points: User Query → Vector Search → Document Retrieval → Context Injection → Model Inference → Response.
Retrieval-augmented fine-tuning (RAFT) represents a paradigm shift from query-time retrieval to training-time knowledge integration. Instead of fetching information at inference, RAFT embeds domain knowledge directly into the model’s parameters during fine-tuning.
At Uniphore, our RAFT implementation leverages LoRA (Low-Rank Adaptation) techniques to make the fine-tuning process more efficient and manageable. Consider this comparison, taken from a Fortune 500 telecommunications company that switched from an LLM + RAG approach to our innovative SLM + RAFT model:
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LMM + RAG (Before) |
SLM + RAFT (After) |
|---|---|
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Latency: 2300 ms |
Latency: 180 ms |
The numbers don’t lie; nor are they the exception to the rule. With SLM + RAFT, businesses can (and do) achieve significant improvements in response latency, accuracy, infrastructure cost, and other factors critical to developing and deploying domain-specific AI at scale.
SLM + RAFT is the clear AI architecture of tomorrow
While the LLM narrative has become well established—and equally well promoted by hyperscaler vendors—the reality for enterprise users is a far cry from what was promised. LLMs, even when paired with RAG, simply fall short in too many critical areas to be trusted in production scale AI. It’s a problem that no amount of optimization can solve completely because its roots start at the architectural level.
SLMs, particularly when paired with RAFT, represents a revolutionary shift in AI architecture. By directly embedding domain knowledge during a model’s fine-tuning, this revolutionary approach enables faster responses, higher accuracy, and greater cost savings. And not just by a little. Enterprises adopting SLMs can slash latency 10x, improve accuracy by double digits, and reduce ownership costs by as much as 90%.
The message is clear: the future belongs to those that embrace an SLM + RAFT architecture. And the future’s happening now.
Take a deeper look at SLMs + RAFT
Download the whitepaper, “The Small Model Revolution: How SLMs with RAFT Are Transforming Business AI.”
