Build a Privacy-Focused AI Rig with Consumer GPU(s): Run DeepSeek, Stable Diffusion, LLaMA, and other AI Models Locally in 8-Bit
An AI RIG
Are you looking to run powerful AI models like DeepSeek, LLaMA, or Mistral or other opensource models on your own hardware? Building a home AI PC or rig with consumer-grade GPUs is an excellent way to achieve high-performance AI inference while keeping your data private.
A rig is a custom-built, modular system designed for specific tasks, such as AI inference, that offers greater flexibility than a typical PC. Unlike off-the-shelf PCs, rigs are built with scalability in mind, featuring multiple PCIe slots for additional GPUs and ample space for memory upgrades. This adaptability makes it easier to incrementally enhance performance over time, which is ideal for running demanding AI models like DeepSeek, LLaMA, or Mistral on consumer-grade hardware while keeping your data private.
In this comprehensive guide, we'll walk you through creating a budget-friendly AI inference rig that can handle 8-bit quantized models. By self-hosting these AI models, you avoid sending sensitive data to cloud servers and maintain full control over your information. We’ll cover everything from privacy benefits and hardware recommendations to assembly steps, software setup (Ubuntu Linux + CUDA), and a real-world use case (Nebula, an AI-powered penetration testing tool). Finally, we'll discuss future upgrade considerations and the cost vs. performance trade-offs. Let's dive in!
Why Build Your Own AI Rig? Privacy and Control
Privacy: One of the main reasons to build a local AI rig is to keep your data off the cloud. Recent news has highlighted risks with cloud-based AI models. For example, the popular Chinese AI platform DeepSeek was found to be collecting user queries and sending them back to servers in China. This means anything you ask the AI potentially sensitive personal or business data—is stored on remote servers outside your control. Privacy advocates warn users not to input confidential information into online AI services for exactly this reason. When you rely on cloud AI (be it DeepSeek, ChatGPT, or others), you are effectively giving those companies your data, which could be analyzed or even leaked.
By contrast, running AI models locally on your own rig ensures your conversations and data never leave your machine. As one expert noted, if you install models like DeepSeek’s locally and run them on your hardware, you can interact privately without your data going to the company that made the model.
A self-hosted AI rig means no chat logs on external servers, no third-party policies to worry about, and no need to trust a company’s security measures. Everything stays on-premise. This is especially important for professionals dealing with sensitive data (e.g. legal, medical, or security research information) who cannot risk sharing context with an external AI service.
Control and Reliability: Beyond privacy, a local AI rig gives you full control over your AI environment. You’re not subject to API rate limits, internet outages, or changes in a cloud provider’s pricing or features. You can choose which AI models to run – whether it’s a large language model like LLaMA 2, a specialized model like Mistral 7B, or even a custom model you’ve fine-tuned. Running these models in an 8-bit quantized mode further makes it feasible to use consumer GPUs without sacrificing much performance. And because the rig is yours, you can update or tweak the software as you see fit. In short, self-hosting your AI ensures consistent availability (your AI is online whenever your rig is on) and the freedom to experiment with different models and settings.
Cost Efficiency: If you use AI heavily, building your own rig can be more cost-effective over time than paying for cloud AI services or hardware rentals. While there is an upfront hardware investment, you avoid ongoing subscription fees or usage charges. Moreover, consumer-grade GPUs have become powerful enough to handle many large models at a fraction of the cost of enterprise server GPUs. An RTX 3060 with 12GB VRAM, for instance, can run 7B to 13B parameter models thanks to 8-bit compression techniques. You’ll also avoid the scenario of cloud providers using your data to further train their models (a common practice) instead, you harness the power of AI for yourself, privately and cost-effectively.
Budget-Friendly Hardware for an AI Rig (Roughly $1,180)
Building an AI inference rig doesn't have to break the bank. We will leverage consumer-grade PC components that offer the best bang for your buck. Below is a list of recommended hardware that balances affordability with the performance needed to run modern AI models (with 8-bit quantization in mind):
Motherboard – ASUS B450-F Gaming: (Amazon Link) – A reliable ATX motherboard that supports AMD Ryzen CPUs (AM4 socket) and multiple GPUs if you decide to expand. The ASUS B450-F provides a solid foundation with good power delivery and enough PCIe slots for a GPU and additional peripherals. It supports up to 128GB RAM, has an M.2 slot for NVMe SSDs, and its ATX form factor fits nicely in standard frames or cases. This board is both affordable and gamer-proven, making it a great choice for a budget AI build.
Power Supply – EVGA Supernova 850W (80+ Gold): (Amazon Link) – A high-quality 850W PSU ensures stable power delivery to your components. The EVGA Supernova series is known for reliability and efficiency. 850W gives you plenty of headroom for an RTX 3060 (which draws ~170W max) plus the CPU, motherboard, and drives. It also leaves room for future upgrades like adding a second GPU or a more power-hungry card. An 80+ Gold rating means it runs efficiently (wasting less heat) under load. Stable power is critical for 24/7 AI workloads, and this PSU delivers clean power with built-in protections.
GPU – NVIDIA RTX 3060 12GB: Recommended – The GPU is the heart of your AI rig. We recommend the NVIDIA GeForce RTX 3060 (12GB) for its affordability and ample VRAM. With 12GB of video memory, the RTX 3060 can load and run large language models that have been quantized to 8-bit. Many popular open-source models (7B, 13B parameters, etc.) will comfortably fit in 12GB with 8-bit precision. In fact, at least 8GB VRAM is needed for most modern LLMs, and 12GB is ideal, We tested LLaMA and Mistral models on 12GB cards. The RTX 3060 also offers good compute performance (CUDA cores and tensor cores) for accelerating AI tasks, and it’s significantly more affordable than higher-end GPUs. Tip: If budget allows, an RTX 4060 16GB or RTX 3080 10GB/12GB are alternatives, but the 3060 12GB hits a sweet spot for price/performance.
Memory – 32GB DDR4 RAM: Memory is another important factor for AI workloads. 32GB of RAM is a recommended minimum for a smooth experience. This allows your system to handle the OS, model data overflow, and any additional tools or browsers you have open. Opt for a 32GB (2×16GB) DDR4 kit, ideally at a decent speed (e.g. 3200MHz or 3600MHz) to pair with the B450 motherboard (which supports DDR4). Dual-channel memory will maximize bandwidth, so be sure to install the two sticks in the correct slots as per the motherboard manual (usually A2/B2 slots for dual channel). With 32GB, you'll have enough headroom to load large model weights (which can be several gigabytes) into memory and avoid slow disk swapping. If you plan to run multiple models or other heavy applications simultaneously, you could consider 64GB, but for most single-model inference tasks, 32GB is plenty.
Storage – 1TB SSD (NVMe Recommended): AI models and datasets can be huge, so fast storage is key. We recommend a 1TB Solid State Drive for your rig, preferably an NVMe M.2 SSD that plugs directly into the motherboard. NVMe drives (like a Samsung 970 EVO Plus or WD Black SN770) have high read/write speeds, which will significantly reduce the load times for your models. For example, loading a multi-gigabyte model from an NVMe might take only a few seconds, whereas on a traditional HDD it could take minutes. The ASUS B450-F board has an M.2 slot for NVMe SSDs, making installation easy. If an NVMe drive is out of budget, a SATA SSD is the next best thing (still much faster than an HDD). 1TB capacity provides room for the OS, necessary software, and multiple AI models (which can range from a few GB to tens of GB each). As you experiment with different models (LLaMA variants, Mistral, etc.), you'll appreciate having the extra space.
Processor – High-Performance Budget CPU (AMD Ryzen): For this build, an AMD Ryzen CPU is ideal, given the AM4 motherboard. You don't need the absolute top-end CPU for AI inference (since the GPU does the heavy lifting), but you do want a decent multi-core CPU to handle background processes and feed data to the GPU efficiently. An excellent option is the Intel Core i7‑9700K, an unlocked 8‑core processor that offers robust performance and overclocking potential for both gaming and productivity. Alternatively, if you're looking for a more budget‑friendly solution without compromising on performance, the AMD Ryzen 5‑5600X (6‑core, 12‑thread) remains a great choice.These CPUs offer strong single-thread performance and enough cores to handle tasks like decompressing model files, running the OS, and even light model training or other workloads. They are also relatively power-efficient and won’t overload the B450 VRM. Make sure to update the motherboard BIOS if you choose a newer Ryzen 5000-series CPU (the B450-F can support them with a BIOS update). Note: If you already have an Intel CPU or another platform, that's fine too – just ensure your motherboard supports a full-length PCIe slot for the GPU and at least 32GB RAM. The key is a balanced CPU that won't bottleneck the GPU and can handle ancillary tasks.
Cooling – Air Cooling Solution: To keep things simple and cost-effective, stick with air cooling for your CPU (and case). A good air cooler will maintain safe temperatures during prolonged AI sessions. If your chosen CPU comes with a stock cooler (e.g., AMD's Wraith coolers), that might suffice at stock settings. For better thermals and quieter operation, consider an affordable tower cooler like the Cooler Master Hyper 212 or a Noctua U12S. These are easy to mount and provide excellent cooling for mid-range CPUs. Make sure your cooler is compatible with the AM4 socket. Use a quality thermal paste and ensure the cooler is mounted firmly. For the GPU, the RTX 3060 has its own fans – in an open frame (described below) it should get plenty of airflow. Overall, air cooling is low-maintenance and more than adequate for this rig’s needs. Bonus: Air coolers are generally cheaper and have less that can go wrong compared to liquid AIO coolers (no pumps or liquid to worry about), aligning well with our reliability and budget focus.
Open-Air Frame & Rack – Mounting the System: Instead of a traditional PC case, we recommend using an open-air mining frame for easy assembly and cooling. For example, a steel open-air frame like this one (Amazon Link) can house your motherboard, GPU, PSU, and SSD in a neat, horizontal layout. These frames are designed for multiple GPUs (up to 6 or 8), which means you have the option to add more GPUs in the future. The open design provides excellent airflow – components are exposed to air, which is great for cooling (just be mindful of dust). To organize and secure your setup, you can place the frame on a 3-tier stackable wire rack (Amazon Link) as a makeshift “chassis.” This metal shelving rack is sturdy enough for the rig and any additional equipment. It also allows you to stack multiple rigs or other hardware on different tiers if you expand later. Using an open frame + rack combo is a popular solution for DIY miners and now for AI enthusiasts – it’s inexpensive, modular, and keeps everything cool. (If you prefer a closed case for aesthetics or dust protection, you can use a standard ATX case, but ensure it has good ventilation and space for the GPU and any future expansions.)
Step-by-Step Assembly Guide
Prepare Your Workspace: Find a large, clean table and ground yourself to prevent static discharge (use an anti-static wrist strap or at least touch a metal object before handling components). Lay out your motherboard, CPU, RAM, SSD, GPU, PSU, and frame. Have a screwdriver (typically Phillips #2) ready.
Mount the Motherboard on the Frame: Install the provided motherboard standoffs onto the open-air frame’s base (if they’re not pre-installed). Place the ASUS B450-F motherboard on the standoffs, aligning the screw holes. Use the appropriate screws to secure the motherboard to the frame. (If your frame has an I/O shield or acrylic base, make sure the motherboard’s ports align with any cutout.)
Install the CPU: Unlock the CPU socket lever on the motherboard. For AMD AM4, the lever will lift the socket cover slightly. Carefully take your Ryzen CPU and orient it correctly – there’s a small gold triangle on one corner of the CPU that should align with a triangle mark on the socket. Gently place the CPU into the socket without forcing – it should drop in when properly aligned. Lower the socket lever to lock the CPU in place.
Attach the CPU Cooler: If your CPU came with a stock cooler (e.g., Wraith Stealth/Prism), it likely has pre-applied thermal paste. If you’re using an aftermarket cooler, apply a pea-sized drop of thermal paste on the center of the CPU. Mount the cooler onto the CPU by aligning it with the mounting brackets. For the stock AMD cooler, you’ll tighten the screws diagonally (a few turns each in an X pattern to evenly distribute pressure). For other coolers, follow their specific mounting instructions. Once the heatsink is mounted and secure, plug the CPU cooler’s fan cable into the motherboard header labeled “CPU_FAN.”
Insert the RAM: Locate the DDR4 memory slots on the motherboard. The B450-F typically has four slots; if using two sticks of RAM, use the slots recommended for dual-channel (often 2nd and 4th slot from the CPU, but check the manual – they’re usually color-coded). Open the retaining clips on those slots. Align each 16GB RAM stick with the slot (notch on the stick matches the notch in the slot) and press it down firmly until the clips snap back into place. You should hear/feel a click on each end of the RAM module when it is fully seated.
Install the SSD: For an M.2 NVMe SSD, find the M.2 slot on the motherboard (ASUS B450-F has an M.2 slot usually between the PCIe slots). Unscrew the tiny screw at the end of the slot (keep it handy!). Insert the M.2 SSD at a 30-degree angle into the slot – it only goes in one way, with the gold connectors matching the socket. Once inserted, push the SSD down flat and secure it with the tiny screw at the end. If you have a 2.5” SATA SSD instead, mount it in the frame’s drive bay or any free spot using the provided screws, and connect a SATA data cable from the SSD to a SATA port on the motherboard. Also connect a SATA power cable from the PSU to the SSD.
Mount the Power Supply: Take the EVGA 850W PSU and position it in the frame’s PSU area (often on one side or bottom of the frame). Use the screws that came with the PSU to fasten it to the frame so it doesn’t move. Make sure the PSU’s fan has room to breathe (ideally facing an open side of the frame or upward).
Connect Main Power Cables: From the PSU, connect the 24-pin ATX power cable to the motherboard’s 24-pin socket (usually on the right-hand side of the board). It will click into place. Then connect the 8-pin EPS cable (sometimes 4+4 pin) from the PSU to the CPU power header on the top-left of the motherboard (near the CPU socket). This powers the CPU. Ensure these connectors are fully seated (they can be tight, but a firm push until the latch clicks is needed).
Install the GPU: Take your NVIDIA RTX 3060 12GB graphics card and insert it into the primary PCIe x16 slot on the motherboard (usually the topmost long slot). Push it straight down into the slot until the plastic latch on the end clicks over the card’s edge. If the frame has a brace or slot for securing GPUs, use a thumb screw or normal screw to fasten the GPU’s bracket to the frame so the card is held solidly. The GPU should now be firmly seated.
Connect GPU Power Cables: The RTX 3060 likely requires an 8-pin (or 8+8-pin) PCIe power from the PSU (check your specific card; most 3060 cards have a single 8-pin). Grab the PSU’s PCIe power cable labeled VGA or PCI-E and plug it into the GPU’s power connector(s) until it clicks. Without this, the GPU won’t power on for heavy workloads.
Double-Check Connections: At this stage, verify all connections. CPU cooler fan is plugged in, 24-pin and 8-pin power to motherboard are connected, GPU is seated and has its power cable, SSD is connected (data and power), and optionally any case wires (for open frames, you may have a power button or LEDs) are connected to the motherboard front-panel header. Since you’re on an open frame, if there’s no case power button, you can use the motherboard’s onboard power switch (if it has one) or short the power pins with a screwdriver to start the system when ready. It’s a good idea to also connect a keyboard, mouse, and monitor at this point for setup.
Power On Test: Flip the PSU switch to the "|" ON position. Press the power button (or short the pins) to power on the system. The motherboard and GPU fans should start spinning. If you get a display output on the monitor (BIOS/UEFI screen), congratulations – the hardware assembly is successful! If nothing happens, recheck the power connections and ensure the RAM and GPU are fully seated. Once in BIOS, you can check that the motherboard recognizes the RAM (e.g., “32GB memory”), the CPU, and the SSD. You may set the boot device to your USB (for installing Ubuntu next) if not automatically done.
Optimize Airflow (if needed): In an open-air rig, airflow is naturally unobstructed, which is great. You can optionally attach case fans to the frame (some mining frames support 120mm fans in front of the GPUs). For a single GPU setup, this isn’t usually necessary, but if you have extra fans, you can mount one to blow air across the board and GPU. Connect any fans to the motherboard’s fan headers. The open design plus the wire rack means your rig has 360° of ventilation. Just ensure the area around it isn’t enclosed and keep the rig away from dust and debris.
Software Installation and Setup
With the hardware built and tested, it's time to set up the software environment for our AI rig. We will:
Install Ubuntu Linux as the operating system,
Install NVIDIA drivers and CUDA to enable GPU acceleration, and
Set up the necessary AI frameworks and libraries (with 8-bit quantization support) for running models like DeepSeek, LLaMA, and Mistral.
Installing Ubuntu Linux
Why Ubuntu? Ubuntu (especially LTS versions like 22.04) is a popular choice for AI and development rigs due to its stability and wide support in the machine learning community. Most AI frameworks are well-tested on Linux, and Ubuntu offers a smooth experience with NVIDIA drivers and PyTorch/TensorFlow installations.
Install Process:
First, download the latest Ubuntu LTS ISO from the official website (for example, Ubuntu 22.04 LTS). Use a tool like Rufus (Windows) or Etcher (Windows/Linux/Mac) to create a bootable USB drive with the Ubuntu ISO.
Insert the USB into your new rig and boot from it. You may need to press a key like F8, F11, or Del during boot to access the BIOS boot menu and select the USB drive.
Once the Ubuntu installer loads, select “Install Ubuntu.” Follow the prompts: choose your language, keyboard, and when asked about installation type, it's usually fine to select "Erase disk and install Ubuntu" (assuming this rig is dedicated to Ubuntu – this will format the SSD and install fresh).
Go through the rest of the installer (create a username/password, etc.). We recommend enabling third-party software and updates when prompted (this will automatically install basic GPU driver support and codecs).
After installation, the system will reboot. Remove the USB drive and boot into your new Ubuntu system. Log in with the account you created.
Post-Install Updates: Open a terminal (Ctrl+Alt+T) and run updates to ensure the OS is current:
sudo apt update && sudo apt upgrade -y
Installing NVIDIA Drivers and CUDA
To harness the RTX 3060 for AI, you need NVIDIA’s proprietary driver and the CUDA toolkit. Ubuntu might have already suggested a driver during installation (if you checked third-party software). If not, or to install the latest version, follow these steps:
Add GPU Drivers: Ubuntu provides an "Additional Drivers" GUI (find it in the Software & Updates app) which should detect the NVIDIA card and present the recommended driver (for example, NVIDIA driver 550.144.03 or as of January 16, 2025). You can use that to install with a click. Alternatively, install via terminal:
sudo apt install nvidia-driver-550
(Replace 550 with the current driver version if newer. You can check available versions with ubuntu-drivers devices.)
Install CUDA Toolkit: The CUDA toolkit provides GPU computing libraries (needed for some AI frameworks and for development). You can install the toolkit via apt as well:
sudo apt install nvidia-cuda-toolkit
This will install CUDA (which typically includes the nvcc compiler, etc.). As of Ubuntu 22.04, this might install CUDA 11.x or 12.x depending on the repos. Alternatively, for the absolute latest CUDA, you can download it from NVIDIA’s website and follow their installation runfile or deb instructions. For our purposes, the repo version is usually fine.
Reboot and Verify: After installing drivers and CUDA, reboot the system to load the NVIDIA kernel modules. Then open a terminal and run
nvidia-smi. You should see a readout with your RTX 3060, its driver version, and some stats. For example, it might show something like:
+-----------------------------------------------------------------------------+ | NVIDIA-SMI 550.xx Driver Version: 535.xx CUDA Version: 12.x | | GPU Name Memory ... | | 0 NVIDIA GeForce RTX 3060 12050 MiB ... | +-----------------------------------------------------------------------------+
Setting Up AI Frameworks for 8-Bit Quantization
With the OS and drivers ready, the next step is to install the AI frameworks and tools to run our models. Our goal is to run large language models (LLMs) like LLaMA, Mistral, etc., with 8-bit quantization for efficiency. We'll likely use Python-based libraries for this:
1. Install Python and Essentials: Ubuntu comes with Python (usually Python 3.x). Ensure you have Python 3.10+ (Ubuntu 22.04 has 3.10). You might also want pip:
sudo apt install python3-pip
It's often useful to set up a Python virtual environment for AI work (to avoid package conflicts):
sudo apt install python3-venv
python3 -m venv ~/ai-env
source ~/ai-env/bin/activate
This creates and activates a virtual environment named "ai-env" in your home directory. (You can skip the venv if you prefer to install packages system-wide, but venvs keep things tidy.)
2. Install PyTorch (CUDA version): PyTorch is one of the most popular deep learning frameworks and will likely be used under the hood to run models like LLaMA/Mistral. Install a CUDA-enabled PyTorch via pip. For example, using the official wheel (at time of writing, PyTorch 2.0+):
pip3 install torch torchvision torchaudio --index-url https://download.pytorch.org/whl/cu126
The --extra-index-url ensures you get the version with CUDA (here assuming CUDA 12.6, adjust if needed). Alternatively, you can install via Conda if you prefer that ecosystem.
3. Install Hugging Face Transformers and 8-bit support: Hugging Face's Transformers library makes it easy to download and run pre-trained models. We also want the BitsAndBytes library for 8-bit quantization support. Install these along with Hugging Face Accelerate:
pip install transformers accelerate bitsandbytes
Transformers will let us load models like LlamaForCausalLM or MistralForCausalLM with a one-liner.
Accelerate helps with efficient model loading (especially if using multiple GPUs, which could be a future expansion).
BitsAndBytes is a crucial library here – it enables 8-bit (and even 4-bit) quantization for model weights. Using bitsandbytes, we can load models in 8-bit mode seamlessly. This drastically reduces memory usage while having minimal impact on model performance. For instance, loading a model in 8-bit precision can cut its memory footprint roughly in half. That means a model that might require 16GB in full precision could use about 8GB in 8-bit, allowing it to fit in our 12GB GPU with room to spare.
4. Download or Prepare Your Models: With the frameworks in place, you can now obtain the AI models you want to run:
Via Hugging Face: You can use the
transformerslibrary to download models from Hugging Face Hub. For example, to load a 7B Mistral model in 8-bit:
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
model_name = "mistralai/Mistral-7B-Instruct-v0.2" # example model repo
quant_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto",
quantization_config=quant_config)
tokenizer = AutoTokenizer.from_pretrained(model_name)
This will automatically download the model weights (you might need to accept model terms on Hugging Face and use an access token for some models like LLaMA). The load_in_8bit=True tells Transformers to use 8-bit loading via bitsandbytes. The device_map="auto" will put the model on your GPU.
Via Nebula (for our use-case): If you plan to use the Nebula pentesting tool (described below), the good news is Nebula will handle model downloading for you. On first run, Nebula prompts you to select a model (LLaMA 8B, Mistral 7B, etc.) and will download it from Hugging Face to a cache directory. You just need to have your Hugging Face token ready (Nebula will guide you to set it as an environment variable)
Manual Download: You could also manually download model files (from Hugging Face or other model zoos) and load them locally. For instance, some projects offer 8-bit or 4-bit quantized model files that you can simply download and point your software to.
5. Verify the setup: To test that everything is working, you could try a quick Python snippet to load a small model and generate text:
from transformers import AutoTokenizer, AutoModelForCausalLM, BitsAndBytesConfig
model_name = "mistralai/Mistral-7B-Instruct-v0.2" # example model repo
quant_config = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
model_name, device_map="auto", quantization_config=quant_config
)
tokenizer = AutoTokenizer.from_pretrained(model_name)
inputs = tokenizer("Hello, my name is", return_tensors="pt")
outputs = model.generate(**inputs)
print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
At this point, your rig has the software environment needed to run large models locally. You have Ubuntu running, the NVIDIA CUDA drivers active, and the Python AI stack (PyTorch, Transformers, bitsandbytes) set up for 8-bit model inference.
Tip: When running your own models, monitor GPU memory usage with nvidia-smi in a separate terminal. This will show how much of the 12GB VRAM is used. 8-bit models will use roughly 1 byte per parameter (plus some overhead). For example, a 7B parameter model might use ~7GB in 8-bit, and a 13B model ~13GB (which may slightly exceed 12GB, but techniques like layer offloading or 4-bit can help if needed). Our rig’s 12GB GPU is well-suited for models in the 7-13 billion parameter range with quantization.
Now, let's explore a real-world application that benefits greatly from this setup.
Use Case Example: Nebula – AI-Powered Penetration Testing on Your Rig
To illustrate the power of a self-hosted AI rig, let's look at Nebula, an AI-driven penetration testing assistant. Nebula is a cutting-edge tool that integrates large language models directly into the penetration tester’s workflow. Developed by Beryllium Security, Nebula acts like a smart co-pilot for ethical hackers – helping automate reconnaissance, analyze vulnerabilities, and even suggest exploits, all via natural language interaction in the command-line.
Why Nebula needs a local AI rig: Nebula leverages open-source LLMs under the hood, specifically models like Meta’s LLaMA (8B parameters), Mistral AI’s 7B model, and DeepSeek’s distilled 8B model. These models are downloaded to your machine on first use and then run locally within Nebula. This design is crucial: it ensures that any sensitive data from your security scans or target information never leaves your machine
In penetration testing, confidentiality is paramount, you wouldn't want to send details about a client’s network to a third-party cloud AI service. Nebula, running on your own rig, guarantees privacy by design.
Performance requirements: Nebula’s use of 7B–8B models means it needs a decent GPU to run smoothly. The official requirements call for at least 8GB of GPU memory (12GB recommended). Perfectly aligning with our RTX 3060 12GB build. Users have reported that with a 12GB card, Nebula can load its AI model and operate comfortably, analyzing command outputs and generating suggestions in real-time. The 32GB of system RAM we installed is also helpful, as the models and other processes (like browser automation or running Nmap scans through Nebula) have room to operate without slowdowns.
How Nebula works on the rig: Once you install Nebula (pip install nebula-ai) and obtain your model (Nebula will walk you through selecting and downloading it on first run), using it is as simple as typing commands with an ! prefix in your terminal. For example, in Nebula's CLI you might type: ! scan the target subnet for open ports. The tool will translate that into actual security tool commands (like Nmap), run them, then use the AI model to interpret the results and suggest next steps. All this AI processing is done by the local model on your GPU. Nebula effectively bridges human language and hacking tools, and thanks to your rig’s power, it can do so interactively without needing an internet connection or API calls. In their documentation, the creators highlight that this offline AI capability is “essential for maintaining data privacy during sensitive security operations.”
Nebula in action: With our rig, you could use Nebula to automate a lot of a penetration test. For instance, Nebula can take the output of an Nmap scan (which you run through Nebula itself) and the AI will summarize what it found (open ports, potential vulnerabilities) and recommend what to do next (maybe run a vulnerability scan on a specific service). This is like having a junior analyst working alongside you. And because it’s all local, it works even in isolated lab environments with no internet, and you don’t risk leaking any intel. It’s a perfect example of why a self-hosted AI rig is so powerful: it enables advanced AI-assisted work in sensitive domains that cloud AIs simply can’t be trusted with.
Beyond Nebula: While Nebula is a specialized case, the success of this setup generalizes to many other scenarios:
You can run a local LLM chatbot (say a LLaMA-2 13B chat model) entirely on your rig for brainstorming or coding assistance without OpenAI or others logging your queries.
You could host an AI writing assistant or translator that works offline.
Researchers could experiment with fine-tuning models on private data without uploading it to cloud GPUs.
Any AI application that deals with proprietary data (from customer support transcripts to medical records) can be kept in-house.
Nebula showcases the promise: your rig turns ambitious AI applications into reality, with privacy, control, and no external dependencies.
Future Upgrades and Considerations
Your AI rig is fully functional, but as with any tech project, you might consider future improvements based on your needs and budget. Here are some additional considerations and upgrade paths:
GPU Upgrades or Addition: The RTX 3060 12GB will handle a lot, but you may eventually want to run even larger models (like 30B or 70B parameters) or run multiple models simultaneously. Upgrading to a GPU with more VRAM (e.g., an RTX 3090/4090 with 24GB, or newer consumer GPUs with 16GB+) would allow bigger models or faster inference. The open-air frame and 850W PSU we chose can accommodate adding a second GPU as well. You could, for instance, insert another RTX 3060 or a second-hand RTX 3080 down the line. Keep in mind, multi-GPU setups for inference usually require model parallelism (supported by libraries like Accelerate or DeepSpeed) – it's doable, but model support varies. Still, the ability to just slot in another GPU gives you a clear upgrade path as your AI demands grow.
Memory Expansion: If you find yourself hitting RAM limits (e.g., using swap space or running many processes), consider adding more RAM. Our motherboard has free slots to expand beyond 32GB. Going to 64GB (or even 128GB) could be beneficial for very large models that partially load to CPU memory, or if you plan to do some lightweight training/fine-tuning on the rig. For most inference tasks with a single model, 32GB suffices, but heavy multitasking or larger multi-modal models (which might load huge tokenizers or datasets into RAM) could justify more memory.
Storage and Model Management: Over time, you might accumulate many models (each potentially 5–20+ GB, especially in fp16). Adding a second SSD can help organize data – for example, one SSD for the OS and frequently used models, another for a library of less-used models or datasets. The B450-F board has additional SATA ports, so you could add SATA SSDs easily. Also, keep an eye on new storage technologies; NVMe drives are continually getting faster and larger, and prices are dropping. Fast storage directly translates to quicker model load and save times.
CPU and Platform: If you ever repurpose this rig for more demanding tasks (like training small models, running multiple VMs/containers, etc.), a CPU upgrade might be beneficial. The AM4 platform can accommodate up to a Ryzen 9 5950X (16-core). However, note that beyond inference, if you wanted to train large models, you'd likely look at a different class of hardware (multi-GPU, more cores, more robust motherboard). For inference and moderate workloads, our chosen CPU is fine. One could also consider moving to a newer platform (like AM5 Ryzen or Intel 12th/13th gen) in the future for DDR5 and PCIe 5.0 support, but that would be a new build. The current setup, though based on a slightly older platform, is cost-effective and gets the job done for current models.
Cooling and Noise: Our air-cooled open rig should run relatively cool due to all the airflow. However, open rigs can be a bit noisy (since there’s no case to dampen fan sound). In the future, you could consider upgrading fans to premium low-noise models (like Noctua fans) or even eventually migrating into a case with sound-dampening if the noise or dust becomes an issue. Regular maintenance, like dusting off fans and components, will keep the rig running optimally. The hardware we chose is all air-cooled, which is straightforward. If you ever push the CPU with heavy loads and find temperatures high, upgrading the CPU cooler to a beefier model or even a 240mm AIO could be an option – but for most inference usage, the CPU won’t be maxed out and air cooling is sufficient.
Software Tweaks and Updates: The AI field is rapidly evolving. New optimization techniques (like 4-bit quantization, compiler optimizations, or distillation of models) can further improve what you can do with your hardware. Keep your software up to date: new versions of PyTorch or Transformers often bring performance improvements. For example, research into 4-bit and 3-bit quantization is ongoing – you might soon run even larger models by leveraging those, at some accuracy cost. Our rig is ready for such improvements since you can always update the code. Also, watch for new model releases; there might be a more powerful 7B or 13B model tomorrow that runs even better on your 12GB card.
Cost vs Performance: It's worth reflecting on the cost vs. performance of this build. We selected components with a focus on value. If you had, say, double the budget, you could build an even more powerful rig – but you might get only, for example, 30-40% more performance for 100% more cost. The law of diminishing returns applies. This build is balanced: the GPU is typically the bottleneck for AI, and we allocated most budget there. The other components ensure the GPU can work at full potential. When considering upgrades, weigh the real needs. For instance, an RTX 4090 would let you run larger models or same models faster – but if your main goal is running a personal chatbot or a tool like Nebula, the 3060 12GB already meets that need well. On the flip side, if your time or workloads are mission-critical, the extra investment in a higher-tier GPU could pay off. The good news is, because we chose a decent PSU and an open frame, you can swap GPUs relatively easily in the future.
Fun Fact!: We built our own rigs with this guide, here is a picture!
AI Rig
In summary, your DIY AI rig is a robust starting point. It empowers you to run advanced AI models with full privacy and control. Whether you're probing networks with Nebula, chatting with a local LLaMA 2, or doing data science with large models, you now have the hardware to do it all 100% locally. And as your needs grow, you have room to expand and upgrade without starting from scratch.
By building this AI inference rig, you've essentially created your own “personal AI cloud” at home. You get the horsepower of modern AI models without trusting external providers with your data. From a privacy standpoint, as highlighted with DeepSeek’s example, this is a huge win – your data stays with you. From a functionality standpoint, you can run open-source models like Mistral and LLaMA in 8-bit precision, achieving impressive performance on consumer hardware. And with real-world tools like Nebula leveraging such models locally, the possibilities are endless.
We hope this guide has been informative and empowering. With relatively inexpensive components and some elbow grease, you now have a powerful AI rig at your fingertips. Happy building, and enjoy your private AI computing experience!
Sources:
Matt Burgess and Lily Hay Newman, WIRED – “DeepSeek’s Popular AI App Is Explicitly Sending US Data to China” (Jan 27, 2025) – (Discusses DeepSeek AI’s data privacy issues and the importance of running models locally for privacy).
Beryllium Security Blog – “AI-Powered Penetration Testing: Nebula in Focus and How It Stacks Up” (Feb 4, 2025) – (Introduces Nebula and emphasizes its offline AI model usage to keep data on local machines for security).
Berylliumsec (Nebula GitHub README) – System Requirements for Nebula – (Recommends at least 8GB GPU for running Nebula’s models; tested on 12GB VRAM, confirming the suitability of an RTX 3060 12GB).
Hugging Face Transformers Documentation – bitsandbytes 8-bit quantization – (Notes that 8-bit quantization roughly halves model memory usage, enabling large models to run on smaller GPUs).
