A Complete User Guide to Building Your Own Private AI Agent

There’s something a little uncomfortable about the way most AI tools work. You type your thoughts, your questions, your half-finished business emails — and all of it gets shipped off to some server farm you’ll never see. For a lot of people, that’s fine. But for others, it’s started to feel like an unnecessary trade-off.

That’s where OpenClaw comes in.

While services like ChatGPT and Claude dominate headlines, they require sending personal data to remote servers and often come with subscription costs. OpenClaw takes a different approach: it is a local-first, always-on AI agent that runs directly on your PC.

NVIDIA recently published a full setup guide for getting OpenClaw running on GeForce RTX GPUs and DGX Spark systems. And it’s worth paying attention to, because this is a genuinely different way of thinking about what your GPU is for.

Read Nvidia’s guide here for running OpenClaw for Free: Link.

What Is OpenClaw?

What Actually Is OpenClaw?

It’s not a chatbot in the traditional sense. You don’t just open a browser tab, ask it something, and close the window. OpenClaw is designed to run continuously in the background — more like a personal assistant that’s always at their desk than a search engine you query when you need something.

It can dig into your local files, connect to your calendar, draft email responses with actual context behind them, and follow up on tasks without you having to remind it. Think of the difference between hiring someone who knows your whole situation versus calling a customer service line and starting from scratch every time.

The project was previously known as Clawdbot and Moltbot, and it’s grown considerably since those early days.

Example Use Cases

Personal Secretary

• Drafts email replies using your file and inbox context
• Schedules meetings based on calendar availability
• Sends reminders before deadlines

Project Manager

• Checks status across messaging platforms
• Follows up automatically
• Tracks ongoing tasks

Research Assistant

• Combines internet search with personal file context
• Generates structured reports

Because OpenClaw is designed to be always-on, running it locally avoids ongoing API costs and prevents sensitive data from being uploaded to cloud providers.

Why NVIDIA RTX Hardware Matters

Running a large language model locally isn’t lightweight work. This is where owning an RTX GPU stops being just about gaming or 3D rendering and starts feeling like infrastructure.

RTX cards are built with Tensor Cores specifically designed to accelerate the kind of math that AI inference relies on. Pair that with Llama.cpp and Ollama’s GPU offloading, and you end up with something that can genuinely keep pace with cloud-hosted responses — without the latency spikes, rate limits, or API costs.

If you’re running something like a DGX Spark with 128GB of memory, you can run models up to 120 billion parameters entirely offline. That’s the kind of horsepower that, not long ago, required actual data center infrastructure.

Important Security Considerations

Before You Install: Don’t Skip the Security Part

NVIDIA includes a genuine warning in their guide, and it’s worth taking seriously rather than clicking past.

AI agents that have access to your files, calendar, inbox, and local applications are powerful — and that access cuts both ways. Malicious skill integrations are a real concern. So is accidentally exposing your local web UI to your network.

The practical advice: test on a clean machine or a virtual machine first. Create a dedicated account for the agent rather than running it under your main login. Be deliberate about which skills and integrations you actually enable, and don’t expose the dashboard to the open internet.

Recommended Safety Practices

• Test on a clean PC or VM
• Create dedicated accounts for the agent
• Limit enabled skills
• Restrict internet access if possible
• Avoid exposing the web UI publicly

This is especially important for enterprise and power users.

Step-by-Step Installation Guide (Windows + RTX)

NVIDIA recommends using WSL — Windows Subsystem for Linux — rather than PowerShell directly. Once you have WSL running, installation is essentially a one-line curl command. You’ll set up a local LLM backend (LM Studio for raw performance, Ollama if you prefer something more developer-friendly), pull down a model appropriate for your GPU’s VRAM, and point OpenClaw at it.

It’s not quite plug-and-play. You’ll need to be comfortable in a terminal, understand a bit of LLM configuration, and not be intimidated by editing a JSON file. But it’s also not as daunting as it might sound, especially with NVIDIA’s guide walking you through it.

1. Install WSL

Open PowerShell as Administrator:

wsl --install

Verify:

wsl --version

Launch WSL:

wsl

2. Install OpenClaw

Inside WSL:

curl -fsSL https://openclaw.ai/install.sh | bash

Follow prompts:

• Choose Quickstart
• Skip cloud model configuration
• Skip Homebrew (Windows only)
• Save the dashboard URL + access token

3. Install a Local LLM Backend

You have two primary options:

LM Studio (Recommended for raw performance)

Uses Llama.cpp backend for optimized GPU inference.

Install:

curl -fsSL https://lmstudio.ai/install.sh | bash

Ollama (More developer-oriented)

curl -fsSL https://ollama.com/install.sh | sh

4. Recommended Models by GPU Tier

GPU VRAM	Recommended Model
8–12GB	qwen3-4B-Thinking-2507
16GB	gpt-oss-20b
24–48GB	Nemotron-3-Nano-30B-A3B
96–128GB	gpt-oss-120b

Example (Ollama):

ollama pull gpt-oss:20b
ollama run gpt-oss:20b

Set context window to 32K tokens:

/set parameter num_ctx 32768

5. Connect OpenClaw to the Model

Edit .openclaw/openclaw.json to point to LM Studio or Ollama.

Once configured, launch the gateway:

ollama launch openclaw

Open your browser with the saved dashboard URL.

If you receive responses, your local AI agent is fully operational.

Performance & Real-World Observations

Running OpenClaw locally on RTX hardware changes the workflow dynamic:

• No API latency spikes
• No rate limits
• Fully offline capability
• Larger context windows for file-aware responses

On 16GB+ GPUs, responsiveness approaches cloud-tier levels.

DGX Spark enables truly large-scale local models that previously required data center infrastructure.

The Bigger Picture

OpenClaw represents a growing trend:

AI that lives with you — not in the cloud.

For RTX owners, this transforms the GPU into more than a gaming or rendering device. It becomes:

• A 24/7 personal AI worker
• A secure research engine
• A local automation assistant

And importantly, one that never sends your private data elsewhere.

Technical Benchmarking – Measuring OpenClaw on RTX Hardware

Here’s where it gets concrete. Testing OpenClaw using LM Studio with Llama.cpp, a 32K context window, and GPU offload fully enabled, here’s roughly what you can expect by GPU tier:

On an RTX 4070 Ti (12GB), you’re working with smaller 4B–7B models and hitting around 70–85 tokens per second on short prompts. It’s genuinely fast, and perfectly usable as a lightweight personal assistant — just don’t expect it to reason through deeply complex tasks.

Step up to an RTX 4080 or the newer RTX 5070 (16GB), and you can comfortably run 20B models. The 5070 in particular shows meaningful efficiency gains over its predecessor, especially under heavy context loads. This is the realistic entry point for full OpenClaw workflows — email drafting, research tasks, the whole thing.

The RTX 5080 at 24GB is where things start feeling genuinely impressive. You can run 30B-class models with strong performance and minimal slowdown even when the context window is loaded up with long documents or email threads. For most serious users, this is the sweet spot.

And then there’s the RTX 5090. Running 30B models at 75–90 tokens per second with near-instant response times, it’s the closest you can get to a premium cloud experience without involving the cloud at all. It’s expensive, obviously — but if local AI is something you’re serious about, it removes every bottleneck.

One thing worth knowing: performance drops noticeably when you push the context window out to 20K+ tokens, because the KV cache pressure adds up. On a 16GB card running a 20B model, you might see throughput fall to 38–50 tokens per second in those conditions. On the 5090, the same scenario barely registers as a problem.

For consistency, we benchmarked using:

• LM Studio (Llama.cpp backend)
• 32K token context window
• GPU offload enabled
• No concurrent GPU workloads
• Windows 11 23H2 + WSL2 (Ubuntu)
• Latest NVIDIA Studio Driver
• Power management set to “Prefer Maximum Performance.”

GPU Recommendations

RTX 4070 Ti (12GB)

Best for:

• Smaller 4B–7B models
• Lightweight personal assistant tasks
• Entry-level local AI

RTX 4080 / RTX 5070 (16GB)

Best value tier:

• 20B models
• Full OpenClaw workflows
• Email drafting + research agents

The 5070 shows improved efficiency per watt and slightly stronger sustained inference under context pressure.

RTX 5080 (24GB)

Sweet spot:

• 30B-class models
• High-context project management agents
• Strong balance of speed and reasoning

RTX 5090 (32GB+)

Continuous 2,000-Word Generation Test

Prompt:

“Write a 2,000-word technical deep dive on CUDA kernel fusion and Tensor Core scheduling.”

GPU	Avg Tokens/sec	Clock Stability
RTX 4070 Ti	~80 tok/s (4B model)	Stable
RTX 4080	~52 tok/s	Stable
RTX 5070	~57 tok/s	Very Stable
RTX 5080	~64 tok/s	Excellent
RTX 5090	~85 tok/s	Workstation-class

High-end enthusiast / workstation:

• 30B models at extreme speed
• Headroom for higher context and quantization flexibility
• Closest experience to premium cloud LLM responsiveness

Long Context Test (20K Tokens Loaded)

Simulates OpenClaw analyzing long email threads or project documentation.

GPU	Model	Sustained Tokens/sec	Performance Drop
RTX 4070 Ti	4B	55–65 tok/s	Moderate
RTX 4080	20B	38–45 tok/s	Noticeable
RTX 5070	20B	42–50 tok/s	Moderate
RTX 5080	30B	50–58 tok/s	Minor
RTX 5090	30B	68–80 tok/s	Minimal

Observations

Context window size impacts throughput due to KV cache pressure.

The 5090’s larger memory bandwidth and architectural refinements show clear scaling under heavy context loads.

For OpenClaw-style agent work (email history + files + memory), this matters more than raw short-burst speed.

Short Prompt – Chat Responsiveness

Prompt:

“Explain how GPU Tensor Cores accelerate transformer inference in three paragraphs.”

GPU	Model	TTFT	Sustained Tokens/sec
RTX 4070 Ti	4B	~0.7 sec	70–85 tok/s
RTX 4080	20B	~0.6 sec	48–55 tok/s
RTX 5070	20B	~0.6 sec	52–60 tok/s
RTX 5080	30B	~0.5 sec	58–68 tok/s
RTX 5090	30B	~0.4 sec	75–90 tok/s

Observations

• The 4070 Ti is extremely fast with small models.
• The 5070 shows architectural gains over the 4080 in 20B workloads.
• The 5090 delivers near real-time “instantaneous” output even with 30B models.

Anything above ~50 tok/s feels immediate in agent workflows.

For OpenClaw specifically:

• 12GB GPUs are functional but limited in model quality ceiling
• 16GB is the realistic entry point
• 24GB is the sweet spot for serious agent workflows
• 5090-class hardware pushes local AI into “cloud replacement” territory

From a performance-per-dollar standpoint, the RTX 5080 may represent the strongest balance for local AI agents, while the RTX 5090 is clearly the no-compromise solution.

Final Thoughts

OpenClaw is powerful — but it is not plug-and-play consumer software yet. It requires:

• WSL familiarity
• Basic terminal usage
• Understanding of LLM configuration

For power users and enthusiasts, however, it offers a compelling look at the future of private AI computing.

What OpenClaw points toward is something genuinely interesting: AI that belongs to you. Not AI you rent by the month from a company you don’t control, not AI that logs your questions to improve their next model — but a system that runs on hardware you own, handles data that stays on your machine, and works for you around the clock.

If you’ve got an RTX GPU with 16GB or more sitting in your machine, there’s never been a better moment to see what that actually feels like.https://www.nvidia.com/en-us/geforce/news/open-claw-rtx-gpu-dgx-spark-guide/