Small, specialized, and secure language models are the optimal solution for the majority of business use cases across industries like healthcare, financial services, legal, education, and more.
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Here at Arcee AI, our definition of a small language model (SLM) is anything with a parameter count of 72B or less.
Despite their smaller parameter count, SLMs can outperform large language models (LLMs) when trained on domain-specific tasks. The reduced size makes SLMs much more cost-effective, resource efficient and delivers lower latency than their LLM counterparts.
Cost savings
72B or less, faster time to value
Lower costs, greater accuracy
Business-centric, human-like language
Better benchmark
compared to LLM’s
60% better benchmarks compared to LLMs
Minimal hallucinations
Low latency, fast response time
Relevant, accurate outputs
Fully customizable with your data
Regularly updated and re-trained to meet your needs
Cost-efficient, achieving up to 50-75% savings compared to LLMs
Fast time-to-value with high ROI
Free from third-party API dependencies
Run securely within your chosen environment
Maintain full transparency and control over your data and model
Meet your compliance needs
Guild Education, a leading education and career advancement program provider, sought a GenAI solution to deliver consistent, customized career recommendations to their diverse users. After ruling out closed-source LLMs due to high costs and limited support, they turned to Arcee AI's SLM approach.
Addressed their data privacy and compliance concerns
Allowed for training on their own data and fine-tuning for their specific use case
All at a fraction of the cost of closed-source LLM providers
Our industry-leading small language models (SLMs) have significantly fewer parameters than LLMs which means they are fast and cost-effective. Our selection of models are purpose-built for specific tasks and data.
Try out our SLMs in Model Engine and sign up here for early access to our new intelligent model routing and inference platform, Arcee Conductor (coming in Q1).
Explore Model EngineTransform how your team operates in an AI-powered world. Arcee AI empowers you to tackle complex tasks with purpose-built SLMs.
Book a DemoA ‘Small’ Language Model is anything with a parameter count of 72B or less. Despite their smaller parameter count, SLMs can outperform LLMs when trained on domain-specific tasks. The reduced size makes SLMs much more cost-effective and resource-efficient, and leads to lower latency compared to LLMs.
The main differences are their size, computational requirements, training time, quality of output, and application scope. SLMs are smaller, require less computational power, train faster, and are often more specialized and better for business use cases. LLMs are larger, more resource-intensive, and take longer to train but are good at handling a broader range of tasks with general purpose.
Key benefits include cost efficiency, security, enhanced task-specific performance, and real differentiation for enterprise AI solutions—ideal for task-specific requirements that streamline operations and drive productivity.
Open-Source SLMs: Arcee-SuperNova, Arcee-SuperNova-Medius, Arcee-SuperNove-Lite, Qwen 2.5 0.5B Instruct, Llama 3.2 1B Instruct, Phi-3.5 Mini Instruct, Gemma 2 9B IT
Closed-Source SLMs: GPT4 omni mini, o1-mini, Gemini 1.5 Flash, Claude 3.5 Haik
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