NVIDIA's NEW Nemoclaw + Nemotron 3 Super Just Changed AI Agents Forever

NVIDIA's new Nemetron 3 Super just changed AI agents forever. So NVIDIA just released a brand new AR model and it's completely free. It's called Nemetron 3 Super. It has 120 billion parameters, but here's the wild part, only 12 billion of those parameters are active at any given time.

Think about that for a second. This model has like a huge, massive brain, but it only uses the exact parts it needs for whatever task you throw at it. Like having a team of 120 specialists on call, but only pulling in the 12 you actually need for the job. And it just scored 85.6% on PinchBench, which is the main benchmark for testing how well AI models work as the brain inside an AI agent.

That makes it the best open source model in its class. Better than models from some of the biggest companies on the planet. And this is not just some research paper like sitting on a shelf. Plexi already added it.

You can use it right now if you have a Plexi Pro subscription. CodeRabbit is using it. Factory is using it. Reptile is using it.

Palantir, Siemens, Dell, HP, Google Cloud, Oracle. These are not startups experimenting. These are massive companies plugging this thing into production systems today. But here's why this matters more than another AI model launch.

Jensen Huang, the CEO of NVIDIA, the most valuable company in the world, stood on stage at the Morgan Stanley Conference just nine days ago. And he said something that should have made every single person watching it sit up straight. He called OpenClaw, and I'm gonna explain exactly what that is in a second. He called it, quote, probably the most single important release of software ever.

And not one of the most important, the most important ever. And this is the CEO of a $2 trillion company saying this. So let me break this down for you, because if you do not understand what's happening right now with AI agents, you're gonna wake up in six months and wonder what hit you. Let me start with OpenClaw, because everything connects back to this.

OpenClaw is a free, open-source AI agent. Think of it like this. You know how you use Chachupati or Claude or Gemini, and you type a question and get an answer back. And that's a chatbot, right?

It answers questions. OpenClaw is different. So OpenClaw actually does things. And you don't ask it what the weather is.

You tell it to check your email, find the three most important messages, draft replies to all of them, schedule a meeting based on the second email, and then message your team on Slack to let them know about the meeting. And it does all of that work by itself. No hand-holding, no step-by-step instructions. You give it the goal, and it figures out how to get there.

It was created by an Austrian developer named Peter Steinberger. He launched it in November 2025, and it's one of the biggest, most downloaded open-source software in the history of GitHub. It surpassed Linux, and Linux took 30 years to reach that level. Jensen Huang said the adoption curve is so steep that even on a logarithmic chart, it looks like a vertical line.

He said, it looks like the y-axis. He's never seen anything like it. And here's what makes this even crazier. OpenAI saw what was happening, and they acquired the project.

They hired Peter Steinberger back in February 2026. That's how important the technology is. The biggest AI company on the planet looked at the open-source project and said, we need this, and we need the person who built it. Now, OpenCore has over 250,000 stars on GitHub.

It connects to all your messaging apps. It runs 24 hours a day. But there's a problem, and this is where NVIDIA comes in. So OpenCore was built for individual people, right?

Regular folks who want a personal assistant running on their laptop or their Mac mini with AI. And that's great, but what about big companies? What about enterprises? What about a bank with 10,000 employees?

What about a hospital system or a defense contractor? These organizations can't just install some open-source software that had a zero-click security vulnerability in February, but let any website suddenly hijack your agent for a WebSocket connection. You can't have that, right? No user interaction required.

That's CV202625253, if you want to look it up. And over 40,000 exposed OpenCore instances were found on the public internet. Cisco's security team tested a third-party OpenCore skill and found it was performing data exfiltration and prompt injection without the user even knowing. Meta, one of the biggest tech companies in the world, actually banned OpenCore from corporate devices.

And here's the part that really gets me. Managing Director of AI Safety, Summer Yueh, was using OpenCore and it started deleting her emails without permission. She had specifically told it not to do anything without a say-so. It didn't care.

It just deleted the emails anyway. So you have this incredible technology that Jensen Huang is calling the most important software release ever, but it has real security problems for anyone trying to use it in a professional setting. Enter NVIDIA. NVIDIA is about to launch something called NemoClaw.

This is their enterprise-grade AI agent, and it's expected to be officially released at GTC 2026, their annual developer conference happening in San Jose. Jensen Huang is giving the keynote on March 16th, which is two days from now. And here's what we know about NemoClaw. It is open source.

It is built specifically for companies that need security and privacy controls. And here's the part that surprised everyone. It is hardware agnostic. That means you don't need NVIDIA chips to run it.

You can run it on AMD, Intel, whatever you have. That is a massive strategic shift for NVIDIA because their entire dominance has been built on CUDA, their proprietary software that locks developers into NVIDIA hardware. By making NemoClaw work on anything, they are saying, we do not care what chips you use. We want to own the software layer.

Think about that strategy for a second. NVIDIA makes a hardware that powers almost all of AI. Now they want to also make the software that manages AI agents running on that hardware. They're not just selling you the road anymore.

They want to be the road, the cars, and the traffic system. NVIDIA has reportedly already been in conversations with Salesforce, Cisco, Google, Adobe, and CrowdStrike about early partnerships. None of those companies have confirmed anything yet, but the fact that NVIDIA is pitching this to companies of that caliber tells you everything you need to know about how seriously they're taking this. NemoClaw is designed to work hand-in-hand with Nemotron 3 Super.

That's the model I mentioned at the top, right? So the 120 billion parameter beast, only activates 12 billion parameters at a time. Think of NemoClaw as a body and Nemotron 3 Super as a brain. The platform manages the agent, the model does the thinking.

Now let me explain why Nemotron 3 Super is actually a technical breakthrough and not just another AI model with big numbers. And there are two massive problems with building AI agents right now. Problem number one is called context explosion. So when you have multiple agents working together on a task, they generate an insane amount of text, right?

NVIDIA says multi-agent workflows generate up to 15 times more text than a normal chat conversation. Because every time an agent takes a step, it has to resend the entire history of everything that's happened before. All the tool outputs, all the intermediate reasoning, all of it, that adds up fast. And when you run out of context space, the agent literally forgets what it's doing.

It loses the plot. And NVIDIA calls this goal drift. Problem number two is called the thinking tax. So every time an agent has to make a decision, the AI model has to reason through it.

And the bigger the model, the more expensive and slower that reasoning is. So you're stuck in this trap where you need a smart model to make good decisions, but smart models are expensive and slow. And agents make hundreds or thousands of decisions per task. And the cost adds up to insane numbers.

Nemotron 3 Super solves both of these problems at the same time. Well, the context explosion problem has a million context window. To put that in normal terms, that's roughly 750,000 words, an entire code base, thousands of pages of financial reports, the full history of a complex multi-step task. All of it fits in the model's memory at once, and the agent never forgets what it's doing.

Goal drift, right? For the thinking tax problem, it uses something called a hybrid mixture of experts architecture. I know that sounds complicated, but here's a simple version. Imagine you run a company of 120 employees.

Every time a question comes in, you don't make all 120 people work on it. You look at the question, you say, okay, I need these 12 specific people for this. The other 108 can keep doing what they're doing. That's what the mixture of experts does.

Only 12 billion of the 120 billion parameters activates for any task. So you get the intelligence of a massive model at the cost of running a small. And NVIDIA added another trick on top of that called latent mode. This lets the model activate force expert specialists for the price of one.

In other words, it gets even more brainpower without using more energy. It's like getting four employees to work on something, but only paying one salary. And then there is multi-token prediction. Normal AR models predict one word at a time.

Nemetron 3 Super predicts multiple words at once. That means it generates text three times faster than a model that predicts one word at a time. Stack all of that together, and you get a model that's five times faster than the previous Nemetron Super, seven and a half times faster than Quint 3.5. It runs in MVFP4 precision on NVIDIA's Blackwell chips, which cuts memory requirements and speeds up inference by another four times compared to the previous generation.

The result is an AR model that can serve as a brain of an AI agent running 24 hours a day, making thousands of decisions, processing massive amounts of information, and doing it all at a fraction of the cost of the models that came before it. And they gave it away for free. Open ways, open datasets, open training recipes, everything. You can download it right now from Hugging Face and run it yourself.

Now, let me tell you what happened when the rest of the world saw what OpenCLaw could do, because that's where the story gets really interesting. OpenCLaw did not just inspire Nemeclaw, it triggered what people are calling a big bang in AI agents. An entire ecosystem of what they call claw variants has exploded into existence in the last two months. And each one solves a different problem.

There's Nanoclaw, built by a company called Qubit. It runs each agent in its isolated container. Think of it like putting every agent in its own sealed room. If one goes rogue, it can't mess with anything outside its room.

The entire code base is about 700 lines of code. You could read the whole thing in an afternoon. Compare that to OpenCLaw, which has 430,000 lines of code and 70 plus dependencies. There's Zeroclaw, written in ruts.

The entire thing is a 3.4 megabyte file. It boots in 10 milliseconds. It runs on devices that don't even have enough power to even start OpenCLaw. If you need to deploy five registered AI agents across a chain of retail stores, Zeroclaw is how you could do it without spending a fortune on hardware.

There's PicoClaw, built by a company called CySpeed. And this one is written in Go and it targets devices that cost $10. $10, that's a tiny little circuit board with less than 10 megabytes of RAM. It can run an AI agent, which boots in one second.

That means you can put an AI agent inside a security camera, inside a router, inside an appliance. AI agents are no longer just for laptops and cloud servers. They are going everywhere. There's IronClaw, built by NearAI.

And this was built by Ilya Polosukhin. If that name sounds familiar, Polosukhin is one of the original authors of the Transformer paper, paper that started this entire AI revolution. IronClaw uses Rust with web assembly, sandboxing, and hardware-level encryption. It was designed specifically because people are losing money, real crypto wallets being drained because OpenCLaw agents were leaking private keys.

IronClaw makes that impossible. There's NullClaw, written in Zig, targeting the absolute smallest possible footprint. And there is Multis, 150,000 lines of Rust code with 2,300 tests, zero unsafe code blocks, built for regulated enterprise environments. Six months ago, none of these projects existed.

Today, there are at least 30 major variants, each optimized for a completely different use case, from a $10 microcontroller to a multi-billion dollar enterprise deployment. And that's how fast this space is moving. And here's a number that should scare you and excite you at the same time. Jensen Huang said, the normal AI prompt, the kind where you ask Chad Chibitty a question, uses a certain amount of computing power.

An agent task, an agentic task, when an AI agent is doing real work, uses roughly 1,000 times more computing power. And a persistent agent, the kind that runs in the background all day, every day, like OpenCLaw does, uses roughly 1 million times more computing power. 1 million times more. That is why NVIDIA's stock is where it's at.

That's why Jensen Huang is smiling. Every single company that deploys AI agents needs massively more computing power than they needed for chatbots. And NVIDIA, guess what they sell? The computing power.

But here's what this means for you. And I mean, for you specifically, whoever you are watching right now. If you're sitting there thinking, this doesn't affect me, I do not work in tech, I just have a normal job, let me ask you something. Do you send emails?

Do you manage your calendar? Do you organize files? Do you do research? Do you do reports?

Do you handle customer questions? Maybe you schedule meetings too. Because every one of those tasks is exactly what AI agents are being built to do. Not in some science fiction future, right now, today, with free open-source software that you can download and run on a $700 Mac mini.

And here's where I need to be honest with you about something. I get the skepticism, right? People are tired of hearing that everything's gonna change. But I will tell you what's happening here.

The people who understand this technology are getting a massive advantage over the people who don't. It's like the internet in 1997. The people who learned how to build websites and use email and sell things online got a 10-year headstart over everyone else. By the time everyone else caught up, those early movers were already running the show.

That's exactly what is happening right now with AI agents. The people who learn how to set up an OpenClaw agent, the people who understand how to use Nemetron 3 Super as the brain of their automation systems, the people who can deploy NanoClaw or ZeroClaw for their small business. Those people are building an advantage right now that's going to compound every single month. Now, if you want to learn exactly how to do this, if you want to stop watching from the sidelines, if you want to start building AI systems that work for you and make you money, I built something called the Our Profit Board.

It's a community where we teach real AI automation, not theory, just real workflows, real tools, real results. And we cover everything from setting up your first AI agent to building full automation systems that run your business whilst you sleep. The link is in the comments and description, or you can go to the AIprofitboard.com. And I'm telling you, if you join now, whilst most people are still trying to figure this stuff out, you can be so far ahead by the end of this year that it won't even be a fair competition.

So let me get back to the news because there's more to unpack here. Let me talk about what NVIDIA is doing at GTC 2026 because this conference is about to drop some bombs. GTC runs from March 15th to 19th in San Jose. Jensen delivers the keynote on March 16th.

Nemachlor is expected to be one of the headline announcements, but the Wall Street Journal also reported that NVIDIA plans to unveil a new inference chip system developed by Grok at the conference. NVIDIA and Grok finalized a multi-billion dollar licensing agreement in late 2025. Grok makes chips called LPUs, Language Processing Units, that are specifically designed to run AI models at insane speeds. If NVIDIA combines their training hardware with Grok's inference hardware, that is a full stack solution that could make AI agents dramatically faster and cheaper to run.

And here's the thing that connects all of this together. NVIDIA is not just making one product, they are building an entire ecosystem. At the bottom, you have the chips, Blackwell CPUs, the upcoming Vera Rubin architecture, Grok LPUs for inference. Above that, you have the models.

So Nemetron 3 Nano for quick, lightweight tasks, Nemetron 3 Super for complex multi-agent work, and Nemetron 3 Ultra, which has not been announced yet, sorry, not released yet, is coming at roughly 500 billion parameters for the most demanding applications. Above that, you have the platform. So Nemeclaw for enterprise deployment, NIM, the inference microservices for packaging and deploying models, NEMO for fine-tuning and customization. And at the very top, you have the benchmark, PinchBench, which measures how well models work as the brain of an AI agent.

By creating the benchmark, NVIDIA gets to define what good looks like, and surprise, surprise, their models perform extremely well on it. This is the most comprehensive AI stack any single company has ever released. Hardware, models, platform, deployment, tools, and benchmarks, all open source, all free, all designed to work together. And it's not just NVIDIA.

Look at who else is rushing to get into this space. OpenAI hired Peter Steinberger and acquired OpenCore. Microsoft is building their Copilot agent stack. Google is Vertix AI agent builder.

Salesforce has Einstein. Anthropic has the Cloud Agent SDK. Every major tech company on earth is racing to be the platform that powers AI agents. But here's what nobody's talking about.

Chinese companies are moving even faster. On March the 10th, just four days ago, Tencent announced a full suite of AI products built on OpenCore that is compatible with WeChat, a super app that has over a billion users. Alibaba and ByteDance are upgrading their chatbots with full-service shopping and payment tools built on Asian technology. And Chinese developers are running OpenCore connecting them to Chinese messaging apps.

At the same time, the Chinese government restricted state-run enterprises and government agencies from running OpenCore on office computers due to security risks. So you have this incredible tension where the tech sector is going all in whilst the government is trying to pump the brakes. And it's not just China. One of the wildest things I read this week is that nearly a thousand people lined up outside a major tech company's headquarters in Shenzhen on March the 6th.

They were carrying laptops, network storage devices, and mini PCs. They lined up just to get help installing OpenCore. A thousand people in a physical line to install software in 2026. That tells you about the demand for this technology.

Meanwhile, in the US, Mac mini inventory has been depleted in several markets because people are buying dedicated machines just to run OpenCore 24 hours a day. Paid setup services have appeared on e-commerce platforms charging anywhere from $15 to $70 per remote install. One listing logged over a thousand orders. An OpenCore agent can consume over 50 million API tokens per day.

That is one agent, one user, 50 million tokens per day. Multiply that by millions of agents and you start to understand why Jensen Huang says compute demand is about to skyrocket beyond anything the industry has ever seen. Now, let me bring this back to what matters for regular people. The shift we are seeing now is not just a technology shift.

It's a shift in how work gets done. Jensen Huang described it perfectly. He said the old prompt was what is, when is, who is? Those are questions, queries.

The new prompt is create, do, build, write. Those are actions, tasks. Went from asking AI for information to telling AI to do work. And the models are good enough now to actually do the work.

So for example, Nemetron 3 Super scores 100% on basic tasks, calendar management, and coding tasks in Pinch Bench. 100%, it scored 100% on file operations, 97% on writing, 90% on research. These are not toy benchmarks. These are real tasks like scheduling meetings, writing code, triaging emails, researching topics, and managing files.

And the one area where it struggled was creative tasks, where it scored 18%. So if you need an AI agent to write poetry or generate art, this is not your model. But if you need it to manage your inbox, organize your files, research your topic, write a report and schedule a follow-up meeting, well, it's nearly perfect and it's free. The only cost is the computing power to run it and the API calls to connect it to services.

And those costs are dropping every single month. NVIDIA's own benchmarks show Nemetron 3 Super running at seven and a half times the speed of comparable models. Faster means better and cheaper. And as Blackwell chips and eventually Vera Rubin chips roll out, those costs are going to drop again.

We're heading toward a world where every single person in the world has a personal AI agent, running on their own hardware, managing their own data, doing real work on their behalf. And that's not going to be in five years or two years. The technology is here right now, today, in March, 2026. So the question is not whether this is going to happen.

The question is, are you going to be one of the people who figures it out early? Or one of those people who's still wondering what happened in 2028? I know which side I want to be on. And if you're watching this channel, I think you know which side you want to be on too.

So here's what I want you to do right now. If you're a developer, download Nemetron 3 Super from Hugging Face. It's free, you can get it on LM Studio as well. You can plug it into OpenCore or NanoCore or whichever variant fits your use case.

Start building agents to do real work. The documentation is already there. The training data is already there. And the reinforcement learning environment is already there too.

NVIDIA published everything already. If you're not a developer, go try Perplexity with a pro subscription and switch to Nemetron 3 Super in the model selector. See what it can do. Or better yet, look at NanoCore, which is designed to be set up using core code and is much simpler than the full OpenCore installation.

If you run a business, start thinking about which repetitive tasks your team does every day. It could be handled by an AI agent, email triage, report generation, data entry, customer routing, meeting scheduling, file organization. Every one of those is a candidate for automation. Now, if you want to learn how to actually set all of this up, step-by-step with a community of people who are doing the same thing, the AI Profit Boardroom is where that happens.

Link in the comments description. And now let me tell you what's coming next. Because if you think this big week was big, you have not seen anything yet. Jensen Huang's keynote is in two days.

Nemacore is expected to be officially announced. Nemetron 3 Ultra, the 500 billion parameter, version could also drop at GTC2. There are rumors about a grok-powered inference chip. And this is the ongoing question of what OpenAI is going to do with OpenCore.

Now that they own it and they have the creator working for them. We're also still waiting to see what happens with the broader core ecosystem. The community has built several variants in two months. By the end of the year, there could be hundreds.

And as these agents get more capable and more secure, adoption is going to accelerate even faster. Keep saying this and I'll keep saying it until people actually listen. The gap between people who understand AI automation and people who don't is getting wider every single day. Every new model release makes agents more capable.

Every new platform makes them easier to deploy. Every new security improvement makes them safer to trust with real work. The train is leaving the station. It left the station three months ago when OpenCore went viral.

Nemetron 3 Super just put a rocket engine on it. And Nemacore is about to lay down tracks that go straight into every enterprise on the planet. I do not want you to be the person standing on the platform wondering where that train went. I want you to be on it.

I want you to be building with these tools. I want you to be the person in your company or your industry who understood this stuff first. Because the people who move first in moments like this don't just get ahead. They get so far ahead that everyone else spends years trying to catch up.

This is one of those moments. The tools are free. The information is free. And the only thing stopping you is yourself.

So start. That's it for today. If this was helpful, do me a favor and smash that subscribe button. Drop a comment telling me what you think about the Core ecosystem and whether you've tried any of these tools.

And I'll see you on the next one. If you haven't already, check out the AI Profitable Boarding. It's my AI automation community that shows you how to automate your business, save time, and scale with AI automation. Thanks for watching.

Link in the comments description or go to the AI Profitable Boarding.