Building a Local LLM Powerhouse: AI with Intel Ultra 7 Series 2 & Nvidia RTX 5080

A Deep Dive into Our Linux AI System Build

For those of us eager to harness the power of Large Language Models (LLMs) without relying on cloud services, building a local AI system is becoming increasingly accessible. This article details the hardware choices we made for our latest project – a Linux-based system optimized for running LLMs locally – along with a breakdown of the costs, pros, and cons of each component. All pricing is current as of December 20, 2025. We focused on any mid-level system, with upgradability in mind for stabilized component pricing in the future. If you’re not aware, the news has spread about memory shortages resulting from the high velocity increase in data center demand. This has begun to stress the consumer market as commercial demand has increased for manufacturing of memory for AI system developments.

System Overview & Use Case

Our goal was to create a robust, reliable system capable of running moderately sized LLMs (7B – 13B parameters) with reasonable performance. We prioritized a balance of CPU power, GPU acceleration, ample RAM, and fast storage – all within a manageable budget. The operating system of choice is a Linux distribution of Ubuntu Linux for its excellent Nvidia driver support, package management, and overall efficiency.

AMD has been the current leader in the CPU market, outperforming intel’s current generation offering in gaming and overall market adoption. However, Intel’s Ultra 7 processors, like the 265K, are strategically positioned to excel in AI productivity tasks. The combination of high core counts and robust performance makes Intel CPUs ideal for users heavily involved in AI-driven workflows – think content creators, developers, or researchers utilizing AI tools for video editing, image generation, or machine learning. This is particularly relevant given the surging demand for AI capabilities, as Intel provides a flexible platform to leverage these technologies without sacrificing overall system performance (or your wallet), making it a strong choice for those prioritizing AI alongside traditional computing needs

Hardware Breakdown & Pricing

Here’s a detailed look at the components we selected:

1. CPU:Intel Core Ultra 7 Processor 265K – $289.00 [We bought ours at Walmart, as they had it available at the same price]

• Specs: This processor offers a strong core count and clock speed, crucial for handling the computational demands of LLM inference.
• Why we chose it: LLM processing benefits from multiple cores for parallel tasks. The Ultra 7 provides a good balance of performance and price.
• Relevance to LLMs: CPU handles model loading, pre/post processing of prompts and responses, and offloading tasks from the GPU.

2. Motherboard: ASUS TUF Gaming Z890-PRO WiFi Z890 LGA 1851 ATX Motherboard– $199.99

• Specs: A robust motherboard with PCIe 5.0 support (future-proofing), ample RAM slots, and integrated WiFi 7.
• Why we chose it: Stability and expandability are key. The Z890 chipset supports the latest technologies and provides a solid foundation for our build.
• Relevance to LLMs: Provides the necessary connectivity and bandwidth for all components.

3. RAM: Patriot Viper Venom 32GB (2x16GB) DDR5– $378.86 [We purchased on eBay, so this includes shipping & fees]

• Specs: 32GB of high-speed DDR5 RAM.
• Why we chose it: LLMs are memory hungry. 32GB is a sweet spot for running 7B-13B parameter models without excessive swapping. Faster RAM speeds improve overall system responsiveness. With prices of RAM as high as they are, we’re saving up for another RAM upgrade to get us up to 64 GB.
• Relevance to LLMs: RAM holds the model weights and intermediate calculations during inference. Insufficient RAM will drastically slow down performance.

4. CPU Cooler: Thermalright Aqua Elite 360 ARGB AIO – $57.47

• Specs: A high-performance 360mm All-in-One (AIO) liquid cooler.
• Why we chose it: LLM workloads can put a significant strain on the CPU, causing it to heat up. Effective cooling is essential for maintaining performance and preventing throttling.
• Relevance to LLMs: Maintains CPU performance under sustained load.

5. PSU: be quiet! Power Zone 2 1000W ATX 3.1 PSU | 80 Plus and Cybenetics Platinum Efficiency – $149.90

• Specs: 1000W 80+ Platinum certified power supply with ATX 3.1 support.
• Why we chose it: A high-quality PSU provides stable power delivery to all components, especially the power-hungry GPU. ATX 3.1 ensures compatibility with the latest graphics cards.
• Relevance to LLMs: Provides stable power for all components, crucial for long-running inference tasks.

6. SSD: Samsung 990 Evo Plus 2TB PCIe Gen5/4 NVMe M.2 – $176.99 [Purchased on-sale at Best Buy]

• Specs: A 2TB NVMe SSD with PCIe Gen5 interface.
• Why we chose it: Fast storage is critical for loading models quickly and reducing latency during inference. NVMe SSDs offer significantly faster speeds than traditional SATA SSDs.
• Relevance to LLMs: Stores the LLM models and provides fast access to data.

7. GPU: NVIDIA GeForce RTX 5080 16GB GDDR7 – $999.99 [From Best Buy at MSRP]

• Specs: NVIDIA’s latest generation GPU with 16GB of GDDR7 VRAM.
• Why we chose it: The GPU is the most important component for LLM acceleration. The RTX 5080 provides excellent performance at a reasonable price point. 16GB of VRAM allows us to run larger models.
• Relevance to LLMs: Performs the vast majority of the calculations during LLM inference, significantly reducing response times. Utilizing CUDA and Tensor Cores is essential.

8. Case: Antec C8 Curve Wood – $151.54

• Specs: Stylish and spacious case with good airflow.
• Why we chose it: A well-ventilated case helps keep components cool.
• Relevance to LLMs: Helps maintain optimal operating temperatures.

My Total Estimated Cost: $2,403.74 (before shipping and taxes).

I want to clarify that I didn’t personally pay for any tariffs on my invoices, but it’s important to note that they were likely paid and included in the final price. This pricing is only for reference, and was the price we paid at the time of purchase between December 20 and December 21, 2025. Your pricing may vary.

Pros & Cons of This Build for Local LLMs

Final Thoughts

Building a local LLM system is a rewarding experience. This build provides a solid foundation for experimentation and development. While the initial cost is significant, the benefits of privacy, control, and offline access make it a worthwhile investment for serious AI enthusiasts. We’ll be sharing more detailed information on the build in the coming weeks, so stay tuned!

Important Notes:

• Pricing: Prices fluctuate! These are estimates as of December 20, 2025. Be sure to check current pricing before purchasing. This post and any respective media isn’t updated for current pricing and is only for reference.
• Model Size: The ability to run specific LLMs will depend on their size (number of parameters) and your VRAM capacity.
• Software Setup: This article focuses on hardware. You’ll need to install and configure the necessary software (e.g., CUDA drivers, LLM frameworks like Ollama, etc.) separately. We may have another article walking through the installation process.
• Customization: This is just one example build. You can customize it to fit your specific needs and budget. We opted for parts that were available at reasonable market prices, and may vary from pricing available when you visit those websites.
• Affiliate Links: We may include affiliate links for the products mentioned, which may earn us a commission if you make a purchase. This doesn’t incur any additional cost for you and doesn’t affect any discounts you may qualify for on their respective websites.