\ud83d\udea8 Breaking: First Andrew Ng, now NVIDIA\u2019s own researchers are backing the same two conclusions:
\n– Small Language models are better than LLMs for Agentic AI
\n– Better orchestratrion leads to cost efficient AI Agents<\/p>\n

Here are two papers explaining this \ud83d\udc47<\/p>\n

\ud83d\udccc Paper 1: “SLM is the Future of Agentic AI”
\n– NVIDIA argues that agents don’t need general conversational genius 100% of the time.
\n– Actual agentic workflows are mostly narrow tasks :
\n\u2022 “Format this JSON”
\n\u2022 “Call the weather API”
\n\u2022 “Check if this date is valid”<\/p>\n

The SLM Advantage
\n– For these tasks, Small Language Models (SLMs <10B params) are not just \"good enough\", they are better\n\n\u2022 Latency: They react instantly (critical for multi-step agents)\n\u2022 Cost: 10-30x cheaper per token\n\u2022 Control: Easier to fine-tune for strict protocols than a stubborn giant model\n\nBut wait...\n\n\"If we use small models, won't the agent get dumber?\"\nThis is where the second paper changes the game\n\"Intelligence isn't just raw parameter count; it's about organization\"\n\n\ud83d\udd17 https:\/\/lnkd.in\/dajzm3TV\n\n\ud83d\udccc Paper 2: \"ToolOrchestra\"\nNVIDIA introduced Orchestrator-8B\nIt\u2019s not a genius at everything, but it\u2019s a genius at management. \nIt acts as a \"Router\"\n\nIt analyzes a request and decides: \"Do I need the expensive reasoning model for this? Or can I use a cheap calculator tool?\"\n\n- The Results are Wild\nThey tested this on the \"Humanity's Last Exam\" (HLE) benchmark.\n\u2022 GPT-5 (Monolithic): 35.1% accuracy\n\u2022 Orchestrator-8B (Router): 37.1% accuracy\n\nThe kicker? \nThe Orchestrator system was 2.5x more efficient and cost ~30% of the monolithic baseline\n\nTLDR:\nBuilding \"Agentic Systems\" by just wrapping a prompt around a massive API is a financial dead end\n\nAs usage scales, your margins vanish\nOrchestrated approach keeps intelligence high but slashes the compute bill by ~70%\n\u2022 Monolithic Agents: Expensive, slow, wasteful\n\u2022 The NVIDIA Way: Orchestrate specialized SLMs.\n\n\ud83d\udd17 https:\/\/lnkd.in\/dJc8-Res\n\n\ud83d\udccc Key Insight: Intelligence is about routing to the right tool, not being the tool\n<\/p>\n","protected":false},"excerpt":{"rendered":"

\ud83d\udea8 Breaking: First Andrew Ng, now NVIDIA\u2019s own researchers are backing the same two conclusions: – Small Language models are better than LLMs for Agentic AI – Better orchestratrion leads to cost efficient AI Agents Here are two papers explaining this \ud83d\udc47 \ud83d\udccc Paper 1: “SLM is the Future of Agentic AI” – NVIDIA argues […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":[],"categories":[16],"tags":[],"_links":{"self":[{"href":"https:\/\/jonathandespres.com\/cryonicsrevival\/wp-json\/wp\/v2\/posts\/2208"}],"collection":[{"href":"https:\/\/jonathandespres.com\/cryonicsrevival\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/jonathandespres.com\/cryonicsrevival\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/jonathandespres.com\/cryonicsrevival\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/jonathandespres.com\/cryonicsrevival\/wp-json\/wp\/v2\/comments?post=2208"}],"version-history":[{"count":1,"href":"https:\/\/jonathandespres.com\/cryonicsrevival\/wp-json\/wp\/v2\/posts\/2208\/revisions"}],"predecessor-version":[{"id":2209,"href":"https:\/\/jonathandespres.com\/cryonicsrevival\/wp-json\/wp\/v2\/posts\/2208\/revisions\/2209"}],"wp:attachment":[{"href":"https:\/\/jonathandespres.com\/cryonicsrevival\/wp-json\/wp\/v2\/media?parent=2208"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/jonathandespres.com\/cryonicsrevival\/wp-json\/wp\/v2\/categories?post=2208"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/jonathandespres.com\/cryonicsrevival\/wp-json\/wp\/v2\/tags?post=2208"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}