{"slug": "nemotron-3-nano", "name": "Nemotron 3 Nano", "category": "ai-models", "category_name": "AI models", "description": "", "recommenders": 8, "commenters": 9, "warners": 0, "score": 6.914, "rising": -2.62, "first": "2025-12-15", "latest": "2026-03-22", "why": "Using newest Nemotron3 nano, you can put a 1m tokens (about 3 ish megabytes of text) of pure code dump", "why_is_quote": true, "has_verdict": false, "sentiment": {"recommend": 8, "neutral": 1, "compare": 0, "warn": 0}, "spark": [0, 4, 3, 0, 1, 0, 0, 0, 0, 0, 0, 0], "listed": true, "loved": [], "caveat": "", "short_quote": "Using newest Nemotron3 nano, you can put a 1m tokens (about 3 ish megabytes of text) of pure code dump", "kind": "", "icon": false, "family": "nemotron", "domain": "openrouter.ai", "github_repo": null, "official_url": "", "pricing": "", "aliases": ["Nemotron 3 Nano", "Nemotron-3-Nano-30B-A3B"], "verdict": null, "praise": [{"label": "", "summary": "", "quotes": [{"id": 759658514, "item_id": 47478657, "url": "https://news.ycombinator.com/item?id=47478657", "by": "mkw", "date": "2026-03-22", "version": "3", "stance": "recommend", "quote": "I designed it around the intelligence dense Nemotron 3 Nano 30B", "excerpt": "TLDR I took a stab at leveraging Dan's work and making it more practical: https://github.com/matt-k-wong/mlx-flash 2 bit quantization lobotomizes the model but is impressive nonetheless! Maybe one day we'll be able to have intelligent 2 bit quants... I wonder. my version supports - 4bit quantization, hybrid streaming (Disk + ram), arbitrary model compatibility, tested on Mamba2, and lets up the framework for LM Studi", "context": "", "use_case": "", "switched_from": "", "story": "Flash-Moe: Running a 397B Parameter Model on a Mac with 48GB RAM"}, {"id": 749320992, "item_id": 46832562, "url": "https://news.ycombinator.com/item?id=46832562", "by": "breput", "date": "2026-01-30", "version": "3", "stance": "recommend", "quote": "is a very impressive local model", "excerpt": "Nemotron-3-Nano-30B-A3B[0][1] is a very impressive local model. It is very good with tool calling and works great with Roo Code for local development. It doesn't get a ton of attention on /r/LocalLLaMA but it is worth trying out, even if you have a relatively modest machine. [0] https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 [1] https://huggingface.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF", "context": "", "use_case": "Local model for tool calling", "switched_from": "", "story": "The $100B Megadeal Between OpenAI and Nvidia Is on Ice"}, {"id": 741808544, "item_id": 46363034, "url": "https://news.ycombinator.com/item?id=46363034", "by": "eurekin", "date": "2025-12-22", "version": "3", "stance": "recommend", "quote": "Using newest Nemotron3 nano, you can put a 1m tokens (about 3 ish megabytes of text) of pure code dump", "excerpt": "If you're interested in the large codebase... The best I found so far are extended context models. Using newest Nemotron3 nano, you can put a 1m tokens (about 3 ish megabytes of text) of pure code dump (I use repomix --style markdown) and ask around. That's been one of the biggest wow moments I had with LLMs so far. Much better experience than any RAG I used", "context": "", "use_case": "querying large codebases", "switched_from": "", "story": "Scaling LLMs to Larger Codebases"}, {"id": 740898128, "item_id": 46306133, "url": "https://news.ycombinator.com/item?id=46306133", "by": "sosodev", "date": "2025-12-17", "version": "3", "stance": "recommend", "quote": "I think it fits your requirements for an OSS model", "excerpt": "Nvidia released Nemotron 3 nano recently and I think it fits your requirements for an OSS model: https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 It's extremely fast on good hardware, quite smart, and can support up to 1m context with reasonable accuracy", "context": "", "use_case": "", "switched_from": "", "story": "Gemini 3 Flash: Frontier intelligence built for speed"}, {"id": 740739136, "item_id": 46296196, "url": "https://news.ycombinator.com/item?id=46296196", "by": "mark_l_watson", "date": "2025-12-16", "version": "3", "stance": "recommend", "quote": "It is fast and passed all of my personal tool use tests, and did a good job analyzing code. Love it.", "excerpt": "I used Nemotron 3 nana on LM Studio yesterday on my 32G M2-Pro mac mini. It is fast and passed all of my personal tool use tests, and did a good job analyzing code. Love it. Today I ran a few simple cases on Ollama, but not much real testing.", "context": "", "use_case": "local tool use and code analysis", "switched_from": "", "story": "Nvidia Nemotron 3 Family of Models"}]}], "caveats": [], "who": [{"by": "dajonker", "context": "runs local models on a 32 GB GPU", "url": "https://news.ycombinator.com/item?id=46680815", "date": "2026-01-19"}], "tags": [], "use_cases": ["local model for tool calling", "local model, tool calling", "writing tests locally", "querying large codebases", "local tool use and code analysis", "cost-effective model"], "compared": [{"slug": "roo-code", "name": "Roo Code", "co": 3, "switched_from": 0, "switched_to": 0}, {"slug": "lm-studio", "name": "LM Studio", "co": 2, "switched_from": 0, "switched_to": 0}], "makers": [], "threads": [{"url": "https://news.ycombinator.com/item?id=46831702", "title": "The $100B Megadeal Between OpenAI and Nvidia Is on Ice", "mentions": 3}, {"url": "https://news.ycombinator.com/item?id=46275111", "title": "Nvidia Nemotron 3 Family of Models", "mentions": 2}, {"url": "https://news.ycombinator.com/item?id=47476422", "title": "Flash-Moe: Running a 397B Parameter Model on a Mac with 48GB RAM", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=46679872", "title": "GLM-4.7-Flash", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=46354970", "title": "Scaling LLMs to Larger Codebases", "mentions": 1}, {"url": "https://news.ycombinator.com/item?id=46301851", "title": "Gemini 3 Flash: Frontier intelligence built for speed", "mentions": 1}], "sentiment_by_month": {"2025-11": {}, "2025-12": {"recommend": 4}, "2026-01": {"neutral": 1, "recommend": 3}, "2026-02": {}, "2026-03": {"recommend": 1}, "2026-04": {}, "2026-05": {}, "2026-06": {}, "2026-07": {}, "2026-08": {}, "2026-09": {}, "2026-10": {}}, "versions": [], "version_unnamed": 0, "family_ref": {"slug": "nemotron", "name": "Nemotron"}, "runs_on": null, "lines": [], "quotes": [{"id": 759658514, "item_id": 47478657, "url": "https://news.ycombinator.com/item?id=47478657", "by": "mkw", "date": "2026-03-22", "version": "3", "stance": "recommend", "quote": "I designed it around the intelligence dense Nemotron 3 Nano 30B", "excerpt": "TLDR I took a stab at leveraging Dan's work and making it more practical: https://github.com/matt-k-wong/mlx-flash 2 bit quantization lobotomizes the model but is impressive nonetheless! Maybe one day we'll be able to have intelligent 2 bit quants... I wonder. my version supports - 4bit quantization, hybrid streaming (Disk + ram), arbitrary model compatibility, tested on Mamba2, and lets up the framework for LM Studi", "context": "", "use_case": "", "switched_from": "", "story": "Flash-Moe: Running a 397B Parameter Model on a Mac with 48GB RAM"}, {"id": 749358096, "item_id": 46834881, "url": "https://news.ycombinator.com/item?id=46834881", "by": "superjan", "date": "2026-01-31", "version": "3", "stance": "recommend", "quote": "", "excerpt": "Oh those ghastly model names. https://www.smbc-comics.com/comic/version", "context": "", "use_case": "Local model for tool calling", "switched_from": "", "story": "The $100B Megadeal Between OpenAI and Nvidia Is on Ice"}, {"id": 749327184, "item_id": 46832949, "url": "https://news.ycombinator.com/item?id=46832949", "by": "bhadass", "date": "2026-01-30", "version": "3", "stance": "recommend", "quote": "", "excerpt": "Some of NVIDIA's models also tend to have interesting architectures. For example, usage of the MAMBA architecture instead of purely transformers: https://developer.nvidia.com/blog/inside-nvidia-nemotron-3-techniques-tools-and-data-that-make-it-efficient-and-accurate/", "context": "", "use_case": "local model, tool calling", "switched_from": "", "story": "The $100B Megadeal Between OpenAI and Nvidia Is on Ice"}, {"id": 749320992, "item_id": 46832562, "url": "https://news.ycombinator.com/item?id=46832562", "by": "breput", "date": "2026-01-30", "version": "3", "stance": "recommend", "quote": "is a very impressive local model", "excerpt": "Nemotron-3-Nano-30B-A3B[0][1] is a very impressive local model. It is very good with tool calling and works great with Roo Code for local development. It doesn't get a ton of attention on /r/LocalLLaMA but it is worth trying out, even if you have a relatively modest machine. [0] https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 [1] https://huggingface.co/unsloth/Nemotron-3-Nano-30B-A3B-GGUF", "context": "", "use_case": "Local model for tool calling", "switched_from": "", "story": "The $100B Megadeal Between OpenAI and Nvidia Is on Ice"}, {"id": 746893041, "item_id": 46680815, "url": "https://news.ycombinator.com/item?id=46680815", "by": "dajonker", "date": "2026-01-19", "version": "3", "stance": "neutral", "quote": "Nemotron 3 Nano is supposed to benchmark better but it doesn't really show for the kind of work I throw at it", "excerpt": "Great, I've been experimenting with OpenCode and running local 30B-A3B models on llama.cpp (4 bit) on a 32 GB GPU so there's plenty of VRAM left for 128k context. So far Qwen3-coder gives the me best results. Nemotron 3 Nano is supposed to benchmark better but it doesn't really show for the kind of work I throw at it, mostly \"write tests for this and that method which are not covered yet\". Will give this a try once s", "context": "runs local models on a 32 GB GPU", "use_case": "writing tests locally", "switched_from": "", "story": "GLM-4.7-Flash"}, {"id": 741808544, "item_id": 46363034, "url": "https://news.ycombinator.com/item?id=46363034", "by": "eurekin", "date": "2025-12-22", "version": "3", "stance": "recommend", "quote": "Using newest Nemotron3 nano, you can put a 1m tokens (about 3 ish megabytes of text) of pure code dump", "excerpt": "If you're interested in the large codebase... The best I found so far are extended context models. Using newest Nemotron3 nano, you can put a 1m tokens (about 3 ish megabytes of text) of pure code dump (I use repomix --style markdown) and ask around. That's been one of the biggest wow moments I had with LLMs so far. Much better experience than any RAG I used", "context": "", "use_case": "querying large codebases", "switched_from": "", "story": "Scaling LLMs to Larger Codebases"}, {"id": 740898128, "item_id": 46306133, "url": "https://news.ycombinator.com/item?id=46306133", "by": "sosodev", "date": "2025-12-17", "version": "3", "stance": "recommend", "quote": "I think it fits your requirements for an OSS model", "excerpt": "Nvidia released Nemotron 3 nano recently and I think it fits your requirements for an OSS model: https://huggingface.co/nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16 It's extremely fast on good hardware, quite smart, and can support up to 1m context with reasonable accuracy", "context": "", "use_case": "", "switched_from": "", "story": "Gemini 3 Flash: Frontier intelligence built for speed"}, {"id": 740739136, "item_id": 46296196, "url": "https://news.ycombinator.com/item?id=46296196", "by": "mark_l_watson", "date": "2025-12-16", "version": "3", "stance": "recommend", "quote": "It is fast and passed all of my personal tool use tests, and did a good job analyzing code. Love it.", "excerpt": "I used Nemotron 3 nana on LM Studio yesterday on my 32G M2-Pro mac mini. It is fast and passed all of my personal tool use tests, and did a good job analyzing code. Love it. Today I ran a few simple cases on Ollama, but not much real testing.", "context": "", "use_case": "local tool use and code analysis", "switched_from": "", "story": "Nvidia Nemotron 3 Family of Models"}, {"id": 740447171, "item_id": 46277948, "url": "https://news.ycombinator.com/item?id=46277948", "by": "pants2", "date": "2025-12-15", "version": "3", "stance": "recommend", "quote": "this looks like it has great potential for cost-effectiveness", "excerpt": "If it's intelligence + speed you want, nothing comes close to GPT-OSS-120B on Cerebras or Groq. However, this looks like it has great potential for cost-effectiveness. As of today it's free to use over API on OpenRouter, so a bit unclear what it'll cost when it's not free, but free is free! https://openrouter.ai/nvidia/nemotron-3-nano-30b-a3b:free", "context": "", "use_case": "Cost-effective model", "switched_from": "", "story": "Nvidia Nemotron 3 Family of Models"}]}