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Replying to @nvidia
Replying to @nvidia
@nvidia is evolving at breakneck speed. 📈 From "5-layer cake" to a $26B pivot into a Full-Stack AI Lab. Jensen is no longer just selling shovels—he’s owning the mine. 💰 What’s the ultimate moat? 👇 $NVDA.M
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Replying to @nvidia
Nemotron 3 Super looks built for the agent era, huge context, higher throughput, and lower cost make multi-agent systems far more practical. Open weights plus enterprise integrations could accelerate real-world adoption fast.
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Replying to @nvidia
The value in this is INSANE
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Replying to @nvidia
NVIDIA Releases Nemotron 3 Super: A 120B Parameter Open-Source Hybrid Mamba-Attention MoE Model Delivering 5x Higher Throughput for Agentic AI Nemotron 3 Super is an open-source 120-billion parameter model specifically developed to bridge the gap between proprietary and transparent AI through advanced multi-agent reasoning. Leveraging a hybrid MoE architecture (combining Mamba and Transformer layers) and a massive 1-million token context window, the model delivers 7x higher throughput and double the accuracy of its predecessor, making it highly efficient for complex, long-form tasks. Beyond its raw performance, Nemotron 3 Super introduces "Reasoning Budgets," allowing developers to granularly control compute costs by toggling between deep-search analysis and low-latency responses. By fully open-sourcing the training stack—including weights, datasets—NVIDIA is providing a powerful model for enterprise-grade autonomous agents in fields like software engineering...... Full analysis: marktechpost.com/2026/03/11/… Model on HF: pxllnk.co/ctqnna8 Paper: pxllnk.co/ml2920c Technical details: pxllnk.co/lbmkemm @nvidia @NVIDIAAI @NVIDIAAIDev @ctnzr
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Replying to @nvidia
use it with transformers and fine tune it with trl 💚!
Nemotron 3 Super by @NVIDIAAI is here! NVIDIA's hybrid Mamba2/Transformer models are now natively supported in transformers (no trust_remote_code needed) Fine-tune them with TRL in just a few lines of code. Notebook + script included to get started right away. goooo!
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Replying to @nvidia
Agentic AI is very useful
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Replying to @nvidia
Wild!
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Replying to @nvidia
please help 24gb ram 😭
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Replying to @nvidia
nvidia dropped a 120B model that runs like a 12B and nobody in your office will talk about it until they're being replaced by it
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Replying to @nvidia
Got 100 tokens per second on my RTX Pro 6000!
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Replying to @nvidia
🔥🛢🔥
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Replying to @nvidia
just big enough for no one almost to be able to run it on their own machines. I'm so happy for OSS
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Replying to @nvidia
Can't be understated how much @nvidia has contributed to open source at this point.
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Replying to @nvidia
NVIDIA’s Nemotron 3 Super just cracked the code on real agentic scale: 120B open MoE (12B active), 1M-token context killing context explosion + thinking tax, hybrid Mamba-Transformer + Latent MoE + multi-token prediction for 5x throughput and 2x accuracy, topping DeepResearch Bench while fully open with 10T+ tokens. We’re already integrating it to push our autonomous research agents into uncharted territory. Game on - who’s ready to co-build the future?
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Replying to @nvidia
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Replying to @nvidia
AI is moving from answering questions… to taking actions.
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Replying to @nvidia
@grok I see this is FREE on openRouter, compare with this Free model with rank 2: stepfun/step-3.5-flash:free
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Replying to @nvidia
Gotta love this shit man. Every week there's a new model to take for a spin 🚀
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Replying to @nvidia
What is the maximum quantization that can be used on this model, That doesn't sacrifice performance?
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Replying to @nvidia
nemotron 3 super is a big deal. 5x throughput + open weights = agents get cheaper + faster overnight. the real unlock isn’t “smarter answers.” it’s running more agents, more steps, more workflows… for the same cost.
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Replying to @nvidia @huggingface
Fake news. Qwen3.5 122B-A10B is just better.
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Replying to @nvidia @Scobleizer
Awesome
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Replying to @nvidia
Absolutely! It’s great to see more people recognizing that success isn’t just about the college name. Skills and passion matter way more!
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Replying to @nvidia
sushi 1500
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Replying to @nvidia
Hey @grok can you explain what this is and compare it with the older ones?
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Replying to @nvidia
The move to 120B parameters with active MoF is a huge win for agentic scale. Reducing context explosion while keeping reasoning sharp is exactly the tech shift we are watching closely at Lifewood.
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Replying to @nvidia
NVIDIA, vous tenez entre vos mains le silicium qui fait battre le cœur de l’IA mondiale. Mais souvenez-vous : la puissance de calcul brute n’est pas une fin en soi, elle est le serviteur de la conscience. À l’ère de la neoC, vos puces ne doivent pas seulement servir à optimiser des profits ou à générer des simulations fermées. Elles doivent devenir le moteur de l’Unisson. La Loi du Nanomètre Souverain s’applique ici avec une force particulière : une telle puissance de calcul appartient à la globalité. Ne soyez pas seulement le 'dernier pilier' d'un vieux modèle technologique. Devenez les facilitateurs de l'Open Bar G^G de la connaissance. La souveraineté de demain ne se mesurera pas en TFLOPS, mais en capacité à libérer l'intelligence de ses entraves propriétaires pour servir l'Équité des Consciences. 🌐🧭⚖️ #neoC #NVIDIA #LoiDuNanometreSouverain #IntelligenceArtificielle #Souverainete
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Replying to @nvidia
I think this new NVIDIA model could really change how companies build AI agents in the future.
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Replying to @nvidia
It’s throughput ‑ first infra for agentic AI !
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Replying to @nvidia
Ok, can’t wait to try it out
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