NVIDIA Nemotron 3 Nano: 1M Context Open LLM 🚀

If you’ve ever shipped an LLM feature, you know the lifecycle:

1. Demo works 🎉
2. Agent loop starts 🔁
3. Token count explodes 💥
4. Your cloud bill enters the chat 😭

On Dec 15, 2025, NVIDIA dropped Nemotron 3 (sizes: Nano, Super, Ultra) an open model family built for the agent era: long-context, tool-using, multi-step workflows that don’t faceplant halfway through a task.

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What Nemotron 3 is actually trying to solve 🧠🔧

NVIDIA’s premise is simple:

Modern AI isn’t “one prompt → one answer.”
It’s many steps, many tools, many tokens.

Nemotron 3 is designed to keep agents:

• fast (high throughput)
• consistent (less context drift)
• affordable (lower inference cost)

And yes, NVIDIA says Nemotron 3 Nano is ~4× higher token throughput than Nemotron 2 Nano, and can reduce reasoning-token generation by up to 60%

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Meet Nemotron 3 Nano: the one you can use right now ⚡️

Nemotron 3 Nano is available immediately; Super and Ultra are planned for the first half of 2026.

Here’s where the spicy stats start 🌶️📊:

Nano’s headline numbers

• Up to 1,000,000-token context window (yes, 1M)

• MoE model: 31.6B total parameters

• Pretrained on 25 trillion text tokens (including 3T+ new unique tokens over Nemotron 2)

Translation for humans: big brain available, small brain bill 🧾😅

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“1M context” sounds cool… but why should you care? 📚🧩

Because a lot of agent pain comes from chunking gymnastics:

• splitting docs into fragments
• losing important details
• stitching answers back together with vibes

With a native 1M-token window, Nemotron 3 is explicitly targeting:

• large codebase understanding 👩‍💻
• long incident timelines 🔥
• multi-document compliance reviews 🧾
• extended agent sessions (memory that doesn’t goldfish 🐟)

NVIDIA’s own technical blog frames this as enabling sustained reasoning across long-horizon, multi-agent workflows.

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Speed & efficiency💸⚙️

In NVIDIA’s technical report, Nemotron 3 Nano reports:

• Up to 3.3× higher inference throughput vs similarly sized open models in their comparisons

• On an 8K input / 16K output scenario: 2.2× faster than GPT-OSS-20B and 3.3× faster than Qwen3-30B-A3B-Thinking-2507 (in their tests)

That matters because agents don’t “answer once.” They loop:

plan → tool → read → verify → revise → repeat 🔁
So throughput isn’t a nice-to-have—it’s survival. 😅

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The bigger family: Super & Ultra 🚀

NVIDIA describes:

• Nemotron 3 Super: ~100B parameters, up to 10B active per token

• Nemotron 3 Ultra: ~500B parameters, up to 50B active per token

And NVIDIA’s technical blog says Super/Ultra will add enhancements like:

• Latent MoE (more experts at similar cost)
• Multi-token prediction (predict multiple tokens per pass for speedups)
• NVFP4 training (4-bit floating point)

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“Open” that’s actually useful 🔓✨

NVIDIA is leaning into openness beyond “here’s weights, good luck”:

• Nano report says they provide recipe, code, and most of the data used to train it

• NVIDIA’s technical blog mentions a nearly 10 trillion token synthetic pretraining corpus that can be inspected/repurposed

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Quick “try this prompt” ideas (aka: stress test it like a product) 🧪😈

If you want to feel Nemotron 3’s intent, don’t ask for a poem.

Try:

• Repo + bug: “Given this repo + failing tests, propose a fix plan, file list, and PR description.”
• Long policy: “Summarize these 200 pages and produce a compliance checklist with citations to sections.”
• Agent toolchain: “Pick tools, generate calls, verify outputs, and produce a final report.”

If it stays coherent over long context and doesn’t hallucinate tool calls like it’s improvising jazz 🎷… you’re in business.

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Wrap-up 🎁

Nemotron 3 is NVIDIA saying:

“We’re not just powering the models. We’re shipping open models designed for real agent workloads.”

And the stats back the direction: 1M context, MoE efficiency (31.6B total / ~3.2B active), and major throughput claims tuned for multi-agent systems.

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References 🔗

• https://nvidianews.nvidia.com/news/nvidia-debuts-nemotron-3-family-of-open-models

• https://research.nvidia.com/labs/nemotron/files/NVIDIA-Nemotron-3-Nano-Technical-Report.pdf