NemoClaw: NVIDIA bets on OpenClaw as the enterprise AI operating system

Just over a month ago, we published a deep analysis of how OpenClaw changed the rules of agentic engineering. In that article, we documented how a side project from an Austrian engineer became the most popular AI agent framework in history, with over 192,000 stars on GitHub.

What we didn’t anticipate was how fast NVIDIA would jump on board.

On March 16, 2026, during the GTC 2026 keynote, Jensen Huang dedicated an entire section of his presentation to OpenClaw. Not to praise it from afar — to announce NemoClaw, NVIDIA’s enterprise answer to making OpenClaw production-ready for corporations.

”Every company needs an OpenClaw strategy”

That was Huang’s exact quote on stage at the SAP Center. And he didn’t frame it as a prediction — he stated it as an imperative:

“Just as we all needed a Linux strategy, an HTTP/HTML strategy, a Kubernetes strategy — every company needs an agentic systems strategy, an OpenClaw strategy.”

The comparison isn’t trivial. NVIDIA is positioning OpenClaw in the same category as Linux and Kubernetes: foundational infrastructure that defines technological eras. And NemoClaw is their contribution to making it a reality in the enterprise world.

What is NemoClaw exactly?

NemoClaw is an enterprise reference stack built on top of OpenClaw. In essence, it takes everything that makes OpenClaw great — autonomous agents, modular skill system, persistent memory, LLM integration — and adds the layers an enterprise needs for production deployment:

OpenShell: the secure runtime

OpenShell is the runtime that executes agents within a controlled environment. Think Docker but for AI agents: isolation, resource control, and execution boundaries.

Policy engine

Granular control over what each agent can do. Can this agent send emails? Can it access the filesystem? Can it make external HTTP calls? Everything is defined in declarative policies.

Network guardrails

Infrastructure-level network security. Agents can’t communicate with unauthorized services, and all traffic is subject to inspection and logging.

Privacy router

Perhaps the most critical piece for enterprise adoption. The privacy router ensures sensitive data never leaves the company perimeter. When an agent needs to use an external LLM, the router filters confidential information before sending the request.

The operating system for agentic computers

In our previous article, we described how OpenClaw works as an instrumentation layer for LLMs — “it gives the model hands and memory.” NemoClaw takes that metaphor a step further: it’s the operating system for agent-based computers.

Just as Windows democratized the PC and Kubernetes democratized the cloud, NemoClaw aspires to democratize agentic AI in the enterprise. The platform:

• Manages resources — allocates compute, memory, and model access for each agent
• Accesses the filesystem in a controlled manner — with per-agent permissions
• Schedules tasks — native scheduling for recurring workflows
• Breaks down problems step by step — built-in multi-agent orchestration
• Communicates across multiple formats — text, email, voice, internal APIs

Hardware-agnostic, optimized for NVIDIA

An important detail: NemoClaw doesn’t require NVIDIA GPUs to run. It’s hardware-agnostic by design. Companies can run it regardless of their existing infrastructure.

That said, it’s optimized for the NVIDIA ecosystem. When running on NVIDIA hardware, NemoClaw automatically integrates with:

• Nemotron models — including Nemotron 3 (global top-3) and the upcoming Nemotron 4
• NeMo — NVIDIA’s AI software suite for fine-tuning and deployment
• GPU acceleration — optimized inference with CUDA

This creates an interesting flywheel effect: OpenClaw is open source and runs on any hardware, but the premium experience is in the NVIDIA ecosystem.

From Steinberger to OpenAI to NVIDIA

The history of alliances around OpenClaw is fascinating:

1. Peter Steinberger creates OpenClaw as a personal project (November 2025)
2. The project goes viral, accumulating 192k+ stars on GitHub
3. Steinberger joins OpenAI (February 2026). Sam Altman announces OpenClaw will live as an open source project in a foundation that OpenAI will support
4. NVIDIA develops NemoClaw in direct collaboration with Steinberger
5. Huang presents it at GTC 2026 as a centerpiece of NVIDIA’s agentic strategy

In less than five months, OpenClaw went from side project to being backed by the two most influential companies in AI on the planet. That doesn’t happen by accident — it happens when a technology fills a real gap in the market.

The competitive landscape

NemoClaw doesn’t exist in a vacuum. The race to dominate enterprise agent infrastructure is already underway:

• OpenAI launched Frontier in February 2026 — their own platform for enterprises to build and manage AI agents
• Gartner published a report in December 2025 on how agent governance platforms will be crucial infrastructure for enterprise adoption
• IBM published architecture guides for secure agents based on OpenClaw
• Anthropic, Google, and Microsoft are integrating agentic capabilities into their respective cloud platforms

NemoClaw’s differentiator: it’s open source and hardware-agnostic. It doesn’t lock you into a cloud provider or a specific model. That’s exactly what made Kubernetes successful — and NVIDIA knows it.

Current state: alpha with rough edges

NVIDIA is transparent about NemoClaw’s state: it’s an early alpha. On their developer page, the company warns:

“Expect rough edges. We are building toward production-ready sandbox orchestration, but the starting point is getting your own environment up and running.”

That means: it works, it’s usable, but it’s not ready for large-scale enterprise production yet. Which is perfectly honest for a project that launched days ago.

Nemotron: the models behind NemoClaw

NemoClaw can use any LLM, but NVIDIA is pushing its own Nemotron models as the optimal choice:

• Nemotron 3 — currently one of the top three models in the world by benchmarks
• Nemotron 3 Ultra — designed as the best base model for fine-tuning, enabling “sovereign AI” customized by industry or country
• Nemotron 4 — in development, with the Nemotron Coalition grouping global AI labs to accelerate its creation

NVIDIA’s vision is that enterprises don’t rely on generic models like GPT or Claude for everything. With Nemotron as a base + domain-specific fine-tuning, each organization can have its own model optimized for its domain — biology, physics, robotics, legal, you name it.

What we’re doing at My Tech Plan

At My Tech Plan we already use OpenClaw in production — our internal agents manage blog content, CRM, team coordination, and technical deployments. We know firsthand what works and what hurts.

Now we’re going to test NemoClaw. Specifically:

• Installation and setup — what the experience of standing up NemoClaw from scratch looks like
• Comparison with vanilla OpenClaw — what the enterprise stack actually adds vs. configuring everything manually
• Policies and guardrails — how granular the control is and whether it solves the real pain points we’ve encountered
• Performance with Nemotron vs. Claude/GPT — a practical benchmark, not a theoretical one

We’ll publish a full review when we have results. If you’re evaluating NemoClaw for your company, follow our blog — we’ll give you the honest perspective of a team that already runs agents in production.

The inflection point

When we wrote about OpenClaw in February, we closed by saying “the question is no longer whether autonomous agents will change software engineering — the question is whether you’re prepared to design systems that include them.”

A month later, NVIDIA answered that question for the entire industry: yes, and here are the enterprise tools to do it.

NemoClaw is alpha. It has rough edges. But it represents something much bigger than a product: it’s the definitive signal that agentic AI has stopped being an experiment and has become infrastructure.

And at My Tech Plan, we’re ready to test it.