AI Shifts to Small Models for Better Speed and Savings
- Sara Habib
- February 25, 2025
- 2:09 pm
- 48
- Technology
Small language models (SLMs) are changing the AI landscape. While large language models (LLMs) like ChatGPT and Gemini have dominated discussions, businesses are now shifting focus. They need AI solutions that are faster, cheaper, and more specific. That’s where SLMs come in.
The Rise of Small Language Models
SLMs are AI models designed for specialized tasks. Unlike LLMs, they don’t need vast amounts of general knowledge. Instead, they focus on specific industries, making them more efficient and cost-effective.
According to Jahan Ali, CEO of MobileLive, “SLMs allow us to train AI on domain-specific knowledge. This makes them more effective for real-world business needs.”
Below are small-scale language models that are lightweight compared to their larger counterparts. These are commonly used when resources are limited or when faster inference is required.
1. o3-Mini
- Parameters: Not publicly disclosed
- Publisher: OpenAI
- Key Features: Released in January 2025, o3-Mini is designed for advanced reasoning tasks. It can decompose complex problems into manageable parts, enhancing problem-solving efficiency.
2. DeepSeek-R1
- Parameters: 671 billion (total); 37 billion (active per token)
- Publisher: DeepSeek
- Key Features: Launched in January 2025, DeepSeek-R1 emphasizes logical inference and mathematical reasoning. Despite its large total parameter count, its architecture activates a smaller subset per token, optimizing efficiency.
3. Phi-4
- Parameters: Not publicly disclosed
- Publisher: Microsoft
- Key Features: Introduced in early 2025, Phi-4 excels in mathematical reasoning and natural language processing tasks, outperforming larger models in specific benchmarks.
4. Gemma 2
- Parameters: Available in 2B, 9B, and 27B variants
- Publisher: Google
- Key Features: Released in mid-2024, Gemma 2 is an open-source model optimized for various NLP tasks, offering a balance between performance and resource efficiency.
5. Mistral Small 3
- Parameters: 24 billion
- Publisher: Mistral AI
- Key Features: Launched in January 2025, Mistral Small 3 is benchmarked as a leading model in the sub-70B parameter category, delivering capabilities comparable to larger models.
Why Businesses Prefer Small AI Models
Many companies hesitate to use LLMs due to high costs and security concerns. Large models require extensive computing power, leading to higher expenses. SLMs, however, are lightweight and can run on local devices.
Avi Baum, CTO of Hailo, explains, “SLMs maintain strong reasoning capabilities while being efficient. They don’t need cloud computing, reducing security risks.”
The Role of SLMs in Agentic AI
SLMs are crucial for the rise of agentic AI. These AI agents make autonomous decisions based on real-time data. For this, they need models that are fast, specialized, and lightweight.
As Stu Robarts wrote in Verdict, “SLMs suit agentic AI due to their higher accuracy and lower computing power needs.” This means AI agents can operate independently, optimizing business processes.
Real-World Applications of SLMs
SLMs are already proving their value in various industries:
Healthcare: AI models assist in diagnosing diseases with greater precision.
Finance: AI agents analyze markets and execute trades in real-time.
Customer Service: AI-powered assistants understand industry jargon and improve response accuracy.
Logistics: AI solutions optimize supply chains, tracking deliveries and inventory.
Shahid Ahmed, EVP at NTT New Ventures, highlights how SLMs transform smart factories. “An AI agent can detect equipment failures and schedule maintenance without human intervention.”
Cost-Effectiveness of Small AI Models
OpenAI, Google, and Anthropic have invested billions in large models. But many now question the return on investment. The shift toward SLMs is driven by their affordability and efficiency.
Ali argues, “Why spend millions on LLMs when a smaller, cheaper model delivers better business results?” SLMs consume fewer resources while maintaining high accuracy.
Challenges of Small Language Models
Despite their benefits, SLMs face some hurdles:
Training Data: High-quality, domain-specific data is essential for accuracy.
Complex Tasks: SLMs struggle with tasks requiring broad contextual knowledge.
Yuval Illuz, AI expert at OurCrowd, says, “The key to success is curating the right training data. Without it, an SLM quickly becomes unreliable.” Businesses must continuously update AI models with real-world data.
The Future of AI: LLMs vs. SLMs
The future of AI is a hybrid approach. Companies will use both LLMs and SLMs. LLMs will handle general tasks, while SLMs will drive business-critical operations.
As Ali puts it, “The goal isn’t just smarter AI—it’s AI that works for businesses.” SLMs prove that smaller, more focused models can deliver better results.
Final Thoughts
AI innovation is moving toward efficiency and real-world impact. Businesses no longer chase the biggest models. Instead, they invest in AI that meets their needs. SLMs are leading this transformation, showing that sometimes, less is more.
