NemoClaw Brings Safer Agentic AI to the Mini PC
During GTC 2026, NVIDIA CEO Jensen Huang described OpenClaw as “probably the single most important release of software, probably ever.” Formerly known as Clawdbot and Moltbot, OpenClaw opens the door to a future where anyone can have an always-on autonomous AI agent just a tap away.
But while OpenClaw unlocks exciting new possibilities, it also brings practical concerns. Sensitive information may pass through third-party cloud services, raising privacy and security considerations. It can also consume tokens quickly, and for heavier workloads, costs can rise fast.
That is where NVIDIA NemoClaw comes in—an enterprise-grade AI agent framework built on OpenClaw, with additional security and performance controls.
Running NVIDIA NemoClaw, however, requires the right hardware: a system that can operate reliably for long hours and deliver the performance needed to handle AI workloads locally.
In this article, we explore how to choose the right AI agent for your needs—OpenClaw or NemoClaw—and how to select the right hardware to support it.
NemoClaw Brings Safer Agentic AI, but the Right Hardware is Needed
NVIDIA NemoClaw is an open-source reference stack for running OpenClaw always-on AI assistants more safely. It is designed to make deployment simple, with a one-command setup, while adding privacy, security, and policy controls through NVIDIA OpenShell. NVIDIA describes it as a way to run self-evolving agents with greater control over behavior and data handling.
Why do you need a secure mini PC for NemoClaw?
Because NemoClaw is built for always-on assistants that may continuously access files, tools, networks, and external services. NVIDIA specifically positions it around policy-based privacy and security guardrails, including a hardened sandbox, network and filesystem controls, least-privilege rules, and credential separation so sensitive access is handled more safely.
What is the ASUS Ascent GX10?
The ASUS Ascent GX10 is an ultra-small AI supercomputer. On first glance, it looks quite similar to a mini-PC. In a compact chassis, it houses PC components and cooling hardware, and you’ll find a range of ports tucked on the rear panel.
But the GX10 is no standard PC. You won’t install Windows on it. You can control it with a connected wired keyboard and mouse, but you’re most likely to control it over your network as a network appliance. At the hardware level, it’s built from the ground up for AI development and deployment. Accelerated by the NVIDIA GB10 Grace Blackwell Superchip and the NVIDIA AI software stack, it facilitates seamless integration and deployment. Key for your purposes in building a locally-run agentic AI assistant with OpenClaw, it offers a stunning 128GB of unified memory, putting incredible intelligence at your fingertips.
The Ascent GX10 is scalable, too. NVIDIA® ConnectX®-7 allows two GX10 systems to be linked for handling even larger models. When you need to boost your assistant’s capabilities, the capacity will be there and waiting.
Why makes the ASUS Ascent GX10 ideal for running an agentic AI assistant?
Several factors make the ASUS Ascent GX10 perfect for your NemoClaw setup.
First is the way that it’s designed to be set up and used. This isn’t an all-purpose machine that you’ll be tempted to try and use as both an AI assistant and your main driver, creating resource conflicts and potentially complicated data security. The Ascent GX10 is laser-focused on AI. Built to be controlled over the network, this super-efficient and cool-running compact supercomputer can operate in a safe and secure silo.
But don’t confuse small size with small performance. At the heart of the Ascent GX10 is the NVIDIA GB10 Grace Blackwell Superchip. This is a tightly integrated compute module that delivers 1 Petaflop of AI performance (FP4). Why does this matter? OpenClaw agents can struggle with “analysis paralysis” on standard hardware because of the latency between thinking (inference) and doing (tool-calling). The GX10’s Blackwell Tensor Cores eliminate this bottleneck, allowing your agent to cycle through complex reasoning loops in milliseconds rather than seconds.
The Ascent GX10 also stands out for its memory architecture. For many setups, memory is the primary bottleneck for expanding the capabilities of your agentic AI assistant. Traditional systems force you to split data between your system RAM and your GPU’s VRAM, creating a massive bottleneck during large-scale model inference. The GX10 utilizes 128GB of LPDDR5x coherent system memory, giving you the capacity to load models like Llama 3.1 70B or Nemotron-3 120B entirely into local memory with room to spare for massive 128k context windows.
It’s hard to overstate just how transformative the memory architecture of the Ascent GX10 is. Even if you bought the most powerful consumer graphics card on the market, you’d “only” have 32GB of RAM to work with. Don’t misunderstand us here: you can do an awful lot with 32GB of GDDR7, and the additional compute power and memory bandwidth offered by the desktop-class graphics card might be necessary for your workflow. But the Ascent GX10 offers four times as much memory capacity. This allows you to give your AI agents more intelligence, more reasoning capabilities,more capacity for sustaining longer conversations, and more capability to work with large datasets. All this means that in any AI context where memory capacity is king, the Ascent GX10 is an undeniable standout.
One final reason why the Ascent GX10 is an ideal pick for agentic AI. NVIDIA recently announced NemoClaw, an open source software stack that adds privacy and security controls to OpenClaw. Here at ASUS, we can confirm the GX10 will be a primary “Agent-Ready” platform for this release. NVIDIA NemoClaw will introduce NVIDIA OpenShell, a secure, sandboxed environment that allows OpenClaw to execute terminal commands and manage files with built-in privacy guardrails. This means your agent can autonomously organize your drive or draft local reports without the security risks associated with unmonitored autonomous scripts.
The Ascent GX10 is more affordable than you might expect
The ASUS Ascent GX10 does require a larger investment than a standard mini-PC. But when you compare it other options for accessing this kind of AI performance and raw memory capacity, the GX10 stands out for not only being more affordable, but more compact and easier to deploy.
Right now, the ASUS Ascent GX10 is available in three options. All three are powered by the same NVIDIA Blackwell GPU and 128GB of LPDDR5x unified system memory, but differ when it comes to their storage solution. The 1TB and 2TB options ship with a PCIe 4.0 M.2 2242 SSD, whereas the higher-end 4TB option includes a PCIe 5.0 M.2 2242 SSD. Down the road, you can upgrade the drive if you find that you need more storage space.
Your agentic AI assistant doesn’t need to live in the cloud, dependent on a monthly subscription and a never-ending supply of tokens. Made possible by OpenClaw or NVIDIA NemoClaw, your assistant can work from the security and control over your own network and your hardware. And with the ASUS Ascent GX10, your assistant can offer incredible intelligence and functionality in a professional-grade, energy-efficient, space-saving design.
Agentic AI has fully arrived, bringing an all-new framework for research, work, play, communication, organization, and so much more. If you’re hitting resource limitations when deploy agentic AI with your current hardware, it’s time for a targeted upgrade. The ASUS Ascent GX10 is your turnkey solution for NemoClaw and more, and it’s available today. Follow the links below to purchase one of these compact AI supercomputers today.
FAQ
Q1. What is the relationship between NemoClaw and OpenClaw?
NemoClaw is NVIDIA’s OpenShell plugin for OpenClaw, designed to run OpenClaw within a controlled sandbox environment. OpenClaw handles the core AI assistant functions, while NemoClaw manages the security boundaries, network policies, and inference routing.
Q2. What technologies does NemoClaw use for sandbox isolation?
NemoClaw uses Landlock, seccomp, and network namespace isolation to create its sandbox environment. By default, it follows a deny-by-default access policy, meaning only explicitly permitted resources can be accessed. This prevents data leakage, untrusted code execution, and returns control back to the user.
Q3. Which AI model does NemoClaw use?
By default, NemoClaw routes inference requests through build.nvidia.com to the NVIDIA Nemotron 3 Super 120B model. Operators can also switch to other models without restarting the system.
Q4. How does NemoClaw’s network policy work?
NemoClaw uses YAML-defined network policies and blocks all unknown hosts by default. When an agent attempts to connect to an unauthorized endpoint, the request is surfaced in the TUI for the operator to approve or deny. Approved endpoints are allowed only for the current session and do not modify the underlying policy.
Q5. What use cases is NemoClaw suitable for?
It is well suited for always-on AI assistants that require controlled network access, sandbox testing environments before granting broader permissions, and scenarios that rely on remote GPU deployment for continuous workloads. Enterprises can now trust company data with AI agents with the framework NemoClaw provides.