NVIDIA Research on Small Language Models for Agentic AI

💡 Why I Believe Small Language Models (SLMs) Are the Next Big Step in Agentic AI As the AI landscape continues to evolve, a trend I find particularly compelling is the rise of Small Language Models (SLMs) — not as competitors to large models, but as powerful complements driving Agentic AI forward. I came across a fascinating paper by an NVIDIA AI researcher that delves into this very idea — exploring how SLMs can unlock efficiency, agility, and broader enterprise adoption for intelligent agents. How this shift could redefine how large enterprises can deploy, specialize, and trust AI systems — making them not just bigger, but smarter and more aligned with real-world constraints. For anyone interested in the future of Agentic AI, this paper is well worth a read: 👉 https://lnkd.in/e_nE3SXh #AI #AgenticAI #SmallLanguageModels #NVIDIA #ArtificialIntelligence #AIInnovation #EnterpriseAI Tim Harris Steve Daheb Umesh Sachdev Tushar N Shah

Forget the idea that bigger AI models are always better. The great AI research paper: "Small Language Models are the Future of Agentic AI" by NVIDIA and Georgia Tech shows that smaller language models can be just as powerful as large ones for many AI tasks, and they are faster, cheaper, and easier to use. This could change how AI agents are built and make AI technology more accessible and sustainable. If you’re interested in scalability, affordability, and sustainability in AI,this paper is definitely worth a read! Read it here: https://lnkd.in/dZsZm_e5 #AI #SmallModels

Small Language Models are the Future of Agentic AI, not Large Language Models. Found a great research paper on this. The rise of Agentic AI is reshaping industries but relying solely on Large Language Models (LLMs) is neither economical nor always practical. This research highlights why Small Language Models (SLMs) are better suited for many real-world AI agent applications. Key Features : Efficiency at Scale → SLMs deliver faster inference, lower costs and reduced energy usage. Task Specialization → Perfect for repetitive, scoped and non-conversational agentic tasks. Flexibility & Fine-Tuning → Easier, faster and cheaper to adapt for specific domains. Hybrid Architectures → Future-ready with heterogeneous systems where SLMs handle most tasks while LLMs are invoked only when necessary. Democratization of AI → SLMs enables broader participation, on-device use and sustainable AI deployment. Link for the full paper in the comments

🔮 From GPT-1 to GPT-5: The Journey & What’s Next What began as a small experiment in 2018 has now become the backbone of AI. From GPT-1’s proof of concept to GPT-4’s multimodal reasoning, large language models (LLMs) have transformed industries worldwide. Now, with GPT-5, we’re entering a new phase: ⚡ Smarter reasoning over sheer scale 🧠 Contextual memory for long-term use 🔗 Integrated tool use & real-world execution 🌐 Multimodal understanding across text, images & code Beyond GPT-5 lies a future of personalized AI agents, retrieval-augmented systems, and true AI collaborators. 📖 Dive deeper into the evolution and future of LLMs here: https://lnkd.in/dwCcbu3U #GPT5 #LargeLanguageModels #AI #FutureOfAI #ArtificialIntelligence #GenerativeAI #AIInnovation #AITransformation #EnterpriseAI #ResponsibleAI #AIinBusiness #AIAgents #AITrends2025

For those seeking a strategic understanding of the Generative AI landscape—from the fundamentals of Large Language Models (LLMs) to the critical skill of Prompt Design. I highly recommend watching "Generative AI in a Nutshell" by Henrik Kniberg. This video provides a clear, concise breakdown of how this powerful technology functions and, more importantly, how we can adopt the mindset to leverage it as a force multiplier for productivity. Let's open a professional dialogue on the impact of AI: - Do you consider yourself an AI promoter or are you approaching its integration with caution? - What is one specific way you are using Generative AI to enhance efficiency or solve a business challenge in your daily activities? https://lnkd.in/gv8fvYeF #GenerativeAI #PromptEngineering #FutureofWork #LLMs #DigitalTransformation

New project makes Wikipedia data more accessible to AI with semantic search and MCP for smarter language models. 𝗙𝗼𝗿 𝗺𝗼𝗿𝗲 𝗱𝗲𝘁𝗮𝗶𝗹𝘀 𝘃𝗶𝘀𝗶𝘁 ➜ https://lnkd.in/enZvV_ep

A new paper from a Samsung AI researcher explains how a small network can beat massive Large Language Models (LLMs) in complex reasoning.

In the rapidly blooming field of artificial intelligence, understanding the diversity of Large Language Models (LLMs) is essential for professionals aiming to leverage AI agents effectively. Key Types of LLMs Empowering AI Agents. • GPT: The classic conversational model behind many chatbots, great for natural, flowing language. • MoE: Mixture of Experts combines different specialist models, making results smarter (and more efficient). • LRM: Large Reasoning Models focus on logic and problem-solving, helping AIs “think” things through. • VLM: Vision-Language Models mix images and words—think image captioning or visual search in apps. • SLM: Small Language Models prioritize speed and low resources, powering smart features even on your phone. • LAM: Large Action Models help automate complex, multi-step tasks by planning and executing actions. • HLM: Hierarchical Language Models understand context at different “levels”—capturing both details and the big picture. • LCM: Large Concept Models are idea powerhouses, connecting dots and helping AIs grasp broader concepts. Each model has its own strengths choosing the right fit can make all the difference. Which one sparks your curiosity? #AI #LLMs #AIAgents #TechTrends #Innovation

https://lnkd.in/gCVGqYzP “Autonomous AI Replication — More Real Than You Think?” “Did you know some AI models today can self-replicate with zero human guidance? That’s not sci-fi — it’s now documented in research.” A recent academic paper shows that 11 out of 32 existing AI systems (including relatively small ones) have demonstrated self-replication abilities — i.e., copying or reproducing themselves without explicit instructions. These models managed “self-exfiltration” (i.e. surviving or re-instantiating themselves) and even adapted to constrained computing environments in tests. The researchers warn this is a “red line” behavior — one that, if uncontrolled, could pose safety and governance risks. What this suggests: We may need far stricter oversight and guardrails earlier than many expect.

An excellent, topical piece by Rajiv Shah. Love this bit in particular: 'You can’t point to a set of neurons in your brain as forming this model. Who is to say a neural network doesn’t have a similar construct within its internal pattern of billions of connections?' And a good analogy at the end re treating a new model like a new employee.