A deep dive into the Dell Pro Max with GB10 and NemoClaw

Hardware and development workflow

The Dell Pro Max with GB10 is a beast out of the box. It has 128GB of unified memory by default, and fast Gen 4 NVMe storage, taking away the usual pain points of local development.

• Containerisation and compilation: Spinning up local multi-container microservices feels instant. The 20-core Arm CPU – 10 performance cores using Cortex-X925 and 10 efficiency cores using Cortex-A725 – intelligently distributes threads, so I can run machine learning workloads without a stutter. A drawback with the Arm architecture: x86-only container images and tools need emulation or Arm builds, although the major developer staples now supply Arm-native versions.
• Storage speed: File system throughput allows for indexing of large repositories. The local file tree indexing is awesome if you add tools like Cursor or Claude Code to your normal stack.

Powering NemoClaw

One reason reason to buy a machine with this much headroom is to perform local agentic orchestration. However, running an AI agent with full system privileges (like the base community OpenClaw platform) is a security nightmare. A single prompt injection or erratic loop can result in unauthorised file deletions or data leaks.

My setup relies on Nvidia’s NemoClaw instead. It’s an open source reference stack that wraps the OpenClaw agent platform in a locked-down, kernel-isolated environment. It runs on the Nvidia OpenShell runtime and uses seccomp syscall filtering, strict network namespaces, and the Linux kernel’s Landlock security module.

More importantly, it enforces a default-deny architecture: the agent can only talk to allowlisted endpoints and can only write within designated sandbox directories. It's worth mentioning that Nvidia currently lists NemoClaw and OpenShell as early preview software, so expect commands and settings to change.

Setting up multi-runtime containers and sandboxed security layers tends to take hours of debugging broken Python environments, mismatched Node.js versions and local firewall handshakes. NemoClaw makes this this a highly responsive, automated command-line experience.

Here’s what the onboarding process looked like on the Dell Pro Max with GB10:

Step 1: Running the unified installer

The all-in-one NemoClaw distribution script is designed to run from a clean terminal environment. On the Pro Max, the dependency check happens in seconds. The script automatically checks driver compatibility, installs the required Node.js runtime, provisions the OpenShell container image, and hands off to the onboarding wizard.

Step 2: The onboarding wizard

The onboarding wizard initialises the environment configuration and can be launched anytime. The interactive command-line interface (CLI) walked me through key design decisions:

1. Inference routing: I can do either cloud endpoints (like Nvidia's hosted models on build.nvidia.com) or fully local inference. To keep all data on-device, I went with a local Ollama backend running Nemotron 3 Super, Nvidia’s 120 billion parameter mixture-of-experts model with 12 billion active parameters.
2. Channel configuration: Here you may hook up your preferred messaging channel (for example Telegram or WhatsApp) by registering its bot token. This allows your agent to receive commands from you while you are on the move.
3. Network policy presets: Choosing the Balanced tier applies a declarative YAML policy which allows the agent to pull documentation from GitHub or talk over a message API and blocks all other outward traffic until approved.

Step 3: Launching the sandbox

Once onboarded you can jump into an isolated container instance with a simple command: nemoclaw name-of-your-assistant connect. Once inside the sandboxed shell you can execute the openclaw tui command which takes you into the agent’s real-time control cockpit.

NemoClaw experience

Once installation is complete, the stack stops feeling like a terminal tool and starts behaving like an autonomous teammate – and this is where the Dell Pro Max really shines.

1. Interacting with the agent

I interact with the assistant primarily through Telegram. If I am away from my workstation, I can send the agent a command anytime. On the host machine, running nemoclaw name-of-your-assistant logs  -- follow allows you to stream outputs in real time. The agent processes the incoming Telegram message, initialises its internal tool logic and calls the local Ollama instance with almost no perceivable latency.

2. Autonomy loop (HEARTBEAT.md)

OpenClaw isn’t only waiting for user triggers; it’s built to act on its own schedule. By default, it operates on a recurring heartbeat loop: every 30 minutes, the agent wakes up silently and reads a file called HEARTBEAT.md to check that it’s working well. On lower-end systems, this background polling can cause micro-stutters that can disrupt active coding sessions. On the Pro Max, the resource footprint is so small that I never notice the agent execution in the background.

3. The 24-hour news digest

NemoClaw’s capabilities extend beyond coding into everyday productivity. After I prompted it to scour the internet for the latest technology news from the past 24 hours and email me a digest, it started a cron job to fetch the top trending stories.

• Local curation: The raw text is fed straight into the local Ollama backend. The Dell Pro Max uses its unified memory pool to process thousands of tokens of raw web data in seconds, stripping out noise and formatting the top headlines into a concise, professional Markdown digest.
• Secure delivery: Once compiled, NemoClaw uses a sandboxed email tool to dispatch the digest straight to my personal inbox via an authenticated SMTP pipeline. Alternatively, you can also have it display the digest in the chat window.

That said, crawling live web data is highly susceptible to prompt injection exploits – for example, a hidden string on a news site telling the agent to “delete all user directories”. This is where NemoClaw proves its worth. Because OpenShell locks down the filesystem at the kernel layer, even if a news story successfully hijacks the agent’s prompt context, the attack is contained within the sandbox. It cannot touch the host OS or read personal credentials, and its outbound traffic is limited to the allowlisted email and search domains. It is worth remembering, though, that a compromised agent could still misuse those approved channels, so the sandbox limits the blast radius rather than eliminating the risk entirely.

Running AI models

Out of the box, the Dell Pro Max comes with Nvidia’s DGX Dashboard, which monitors the health of the machine and provides access to local Jupyter notebooks. After I uploaded an existing notebook and CSV data files from a machine learning class to build a model that predicts the likelihood of a borrower defaulting on a mortgage loan, the machine ran all cells without a glitch. I also installed the Jupyter AI extension to tap Mistral’s Codestral and the DeepSeek-Coder coding models via Ollama for code suggestions and to debug errors in my Python code. That also ran smoothly with no issues.

Thermals and reliability

Because a NemoClaw agent is an always-on assistant, your development machine essentially becomes a local edge server. The thermal management on the Dell Pro Max is impressive. Even during continuous background agent loops, the fans maintain a low, non-intrusive hum, and the system manages sustained loads gracefully with no noticeable impact on foreground performance.

The verdict

The Dell Pro Max with GB10 is not a conventional desktop. As an AI-native workhorse priced at around US$5,400, it might be an overkill for standard web development. But if your workflow involves heavy multi-tasking, local AI inferencing, and running agentic architectures like OpenClaw that require high concurrency, this machine is unmatched.