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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.
Reach out today to learn how to get started.
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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
Activeloop specializes in helping enterprises organize complex unstructured data and leverage AI for knowledge retrieval, particularly serving clients in heavily-regulated industries.
Activeloop collaborated with Arcee AI to develop a small language model (SLM) solution for U.S. Patent data, making the vast wealth of information contained in U.S. patents more accessible and navigable for broader audiences.
Addressed data privacy and compliance concerns
50% fewer hallucinations and 2.5x faster response times vs. OpenAI Ada+Pinecone setup
Accelerated deployment from data to production, unlocking business value faster
Arcee Conductor is our intelligent model routing platform. It evaluates your prompt, then sends it to the optimal SLM or LLM–based on domain or task complexity.
The result? Your simpler or routine prompts no longer go to premium models, which slashes your AI spend by 50-200x per prompt.
Transform 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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