Open weights, 4x faster inference, million-token context—NVIDIA’s tiny beast is built for agentic workflows you can run locally.
Dreaming of agentic AI without cloud bills or latency headaches? NVIDIA’s Nemotron 3 Nano just landed as the open-source hero devs have been waiting for[3].
This hybrid Mamba-Transformer MoE model packs a 1M context window, 128K output, and screams at 4x faster inference speeds under the NVIDIA Open Model License. It’s optimized for agentic tasks, crushing benchmarks while staying lightweight enough for edge deployment[3].
For developers, this means building long-context RAG agents or multi-step reasoners without frontier model costs. Run it on consumer hardware, integrate with vLLM or llm-d for production scaling—perfect for turning prototypes into real apps[3][6].
Stack it against DeepSeek R1 or GPT-4o-mini: Nemotron edges out on speed and context, plus full open weights beat proprietary lock-in. As enterprises shift to hybrid SLM+RAG architectures, this sets the edge AI standard[2].
Download weights now, spin up a local inference server, and test on your toughest agent chain. Could this tiny model redefine ‘local-first’ AI?
Source: LLM Stats