🚨 Rethinking AI Agents: The Future is Hybrid, and It's Powered by SLMs! While LLMs get the spotlight, new NVIDIA research argues the future of AI agents is the Small Language Model (SLM). The reason is simple: most agentic tasks are repetitive, narrow, and structured. They don’t always require the heavy compute and cost of LLMs. Why SLMs are useful for agentic AI: ⚡️ Powerful Enough: Modern SLMs (e.g., NVIDIA Nemotron-H, Huggingface SmolLM2) match larger models on critical agent tasks like reasoning and tool calling. 💰 More Economical & Faster: With lower latency and 10-30x cheaper to run, they enable real-time responses with less cloud expenses. Fine-tuning for specialized workflows is also dramatically faster. ⚙️ Operationally Superior: Ideal for modular design, SLMs are perfect for edge and on-device AI, and offer better privacy and user control. They ensure easily formatted outputs for reliable tool use. 📌 Suggested Approach: The path forward isn't about replacing LLMs, but building smarter. - Build hybrid, heterogeneous architectures: modular agents with a mix of models. - Fine-tune SLMs when possible for specific, high-volume skills. - Gradually migrate sub-agents and repetitive tasks from LLMs to SLMs. 📈 The Future is Hybrid: This approach is more than a technical fix. It’s a move toward responsible and sustainable AI that unlocks massive cost savings, broader accessibility, and more scalable systems. 📖 Paper: "SLMs for Agentic AI" https://lnkd.in/gfMq3EEK
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