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    Ts Floyd
    Ts Floyd@ts_floyd3w
    🏢Nvidia🏢Georgia Tech - Georgia Institute of Technology💭AI
    Small Language Models Future Agentic AI

    @ts_floydMost agent calls involve small decisions like routing, classifying, checking, or gating. These tasks do not require frontier models because an agent loop primarily consists of bounded choices rather than deep reasoning. Post-training methods such as SFT and RL against verifiers are becoming more important than raw model size. A 3B parameter model quantized to int4 fits in approximately 1.5 GB and runs on a laptop. API traffic can be distilled into a proprietary model with hard cases escalated. At high volume, owning models becomes more cost-effective than renting within days. TwIL-LM3-Pro by thewebai has 3.6 billion parameters. It performs on par with Qwen3-8B on formal logic tasks and leads VibeThinker-3B on all six tested formal-logic tasks. TwIL-LM3-Pro occupies 2.09 GiB and runs on CPU or 4GB VRAM. Every agent will incorporate a layer of small specialists underneath it. Small Language Models are positioned as the future of Agentic AI. An 18-page PDF containing full details is available. The TwIL-LM3-Pro model is hosted at http://huggingface.co/webAI-Official/TwIL-LM3-Pro.

    View original post

    Small Language Models Future Agentic AI

    Photo by @ts_floyd· Oct 2, 2026· Nvidia

    About this photo

    This is a screenshot of a research paper titled "Small Language Models are the Future of Agentic AI." The focus is the paper's title, authors, and abstract. The mood is academic and informative. A notable detail is the arXiv watermark on the left side. ON-SCREEN TEXT: Small Language Models are the Future of Agentic AI. Peter Belcak, Greg Heinrich, Shizhe Diao, Yonggan Fu, Xin Dong, Saurav Muralidharan, Yingyan Celine Lin, Pavlo Molchanov. NVIDIA Research, Georgia Institute of Technology. [email protected]. Abstract. 1 Introduction. Pre-print. Under review.

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    Photo
    Ts Floyd
    Ts Floyd@ts_floyd3w
    🏢Nvidia🏢Georgia Tech - Georgia Institute of Technology💭AI
    Small Language Models Future Agentic AI

    @ts_floydMost agent calls involve small decisions like routing, classifying, checking, or gating. These tasks do not require frontier models because an agent loop primarily consists of bounded choices rather than deep reasoning. Post-training methods such as SFT and RL against verifiers are becoming more important than raw model size. A 3B parameter model quantized to int4 fits in approximately 1.5 GB and runs on a laptop. API traffic can be distilled into a proprietary model with hard cases escalated. At high volume, owning models becomes more cost-effective than renting within days. TwIL-LM3-Pro by thewebai has 3.6 billion parameters. It performs on par with Qwen3-8B on formal logic tasks and leads VibeThinker-3B on all six tested formal-logic tasks. TwIL-LM3-Pro occupies 2.09 GiB and runs on CPU or 4GB VRAM. Every agent will incorporate a layer of small specialists underneath it. Small Language Models are positioned as the future of Agentic AI. An 18-page PDF containing full details is available. The TwIL-LM3-Pro model is hosted at http://huggingface.co/webAI-Official/TwIL-LM3-Pro.

    View original post

    Small Language Models Future Agentic AI

    Photo by @ts_floyd· Oct 2, 2026· Nvidia

    About this photo

    This is a screenshot of a research paper titled "Small Language Models are the Future of Agentic AI." The focus is the paper's title, authors, and abstract. The mood is academic and informative. A notable detail is the arXiv watermark on the left side. ON-SCREEN TEXT: Small Language Models are the Future of Agentic AI. Peter Belcak, Greg Heinrich, Shizhe Diao, Yonggan Fu, Xin Dong, Saurav Muralidharan, Yingyan Celine Lin, Pavlo Molchanov. NVIDIA Research, Georgia Institute of Technology. [email protected]. Abstract. 1 Introduction. Pre-print. Under review.

    See all Nvidia photosRead the Nvidia wiki

    ?

    More Nvidia photos

    See all Nvidia photos
    REA Reverse Engineer Anything GitHubREA Reverse Engineer Anything GitHubopus-5.5 reverse-engineering toolopus-5.5 reverse-engineering toolOpus 5.5 motion design prompt from r/ClaudeDesignOpus 5.5 motion design prompt from r/ClaudeDesignOpus 5.5 Rust Creative Cloud alternativeOpus 5.5 Rust Creative Cloud alternativeAnthropic Claude motion design promptAnthropic Claude motion design promptOpus 5.5 Motion Design HarnessOpus 5.5 Motion Design HarnessMeaghan Choi Anthropic Meta promptMeaghan Choi Anthropic Meta promptAI Invasion LaunchPoint earningsAI Invasion LaunchPoint earningsAndrej Karpathy Local Language Model Engineering 2026Andrej Karpathy Local Language Model Engineering 2026Opus 5.5 motion design prompt guideOpus 5.5 motion design prompt guideSpaceXAI hiring insights panelSpaceXAI hiring insights panelOpus 5.5 motion design practical guide4Opus 5.5 motion design practical guideClaude motion design.md filesClaude motion design.md filesAI automation for personal tasksAI automation for personal tasksOpus 5.5 Motion Design Prompting TechniquesOpus 5.5 Motion Design Prompting TechniquesClaude Opus 5.5 motion design prompt guideClaude Opus 5.5 motion design prompt guideJEV + Opus 5.5 company brain architectureJEV + Opus 5.5 company brain architectureTypeSafe Jev Loop guide for coding agentsTypeSafe Jev Loop guide for coding agents