Build an AI Workstation: Complete Parts List (2026)
A comprehensive guide to building your own AI workstation. Choose parts for every budget tier, from entry-level to extreme, with specific recommendations.
I built my first dedicated AI workstation three years ago. It was part necessity—cloud costs were eating my budget—and part curiosity. Could a home-built machine really handle serious AI work?
The answer was a definite yes. But I also made expensive mistakes along the way. I bought a GPU that was overkill for my needs. I skimped on RAM and regretted it within weeks. I picked a power supply that barely handled the load.
This guide is everything I wish I’d known before that first build—and updated for the current hardware landscape in 2026. Whether you want to run local LLMs, train models, or just experiment with AI development, I’ll help you build the right machine.
Why Build Your Own AI Workstation?
Before diving into parts lists, let’s consider whether building makes sense for you.
The Case for Local AI
Cost savings at scale. Cloud GPU time adds up fast. A workstation paying for itself within months is common for regular users.
Privacy. Your data never leaves your premises. This matters for sensitive applications.
Availability. No waiting for cloud instances. Your hardware is always ready.
No internet required. Work on planes, in remote locations, wherever.
Learning. Understanding hardware deepens your AI knowledge.
When Cloud Might Be Better
Building doesn’t make sense for everyone:
- Occasional, sporadic AI use
- Need for massive scale beyond single-machine capability
- Tight upfront budget with flexible operating budget
- Requirement for cutting-edge hardware that’s sold out
For most regular AI practitioners, a local workstation makes economic and practical sense. Let’s build one.
What You Need to Know First
Your ideal build depends entirely on what you’ll do with it. Let me break down the use cases.
Use Case Tiers
Tier 1: Running Local LLMs You want to run models like Llama 4 locally for inference—chatting, coding assistance, document analysis.
Requirements: Moderate GPU memory (16-24GB), decent CPU, 32GB+ RAM
Tier 2: Fine-Tuning and Small Training You need to fine-tune open models on your own data, train small custom models.
Requirements: More GPU memory (24-48GB), fast storage, 64GB+ RAM
Tier 3: Serious ML Development You’re doing substantial training, working with large datasets, running multiple experiments.
Requirements: High-end GPU(s) (48GB+), fast CPU, 128GB+ RAM, NVMe storage
Tier 4: Research and Production Professional-grade work that might otherwise require cloud clusters.
Requirements: Multi-GPU setups, maximum memory, redundancy considerations
The Critical Question: What Models Will You Run?
This determines everything. See our LLM landscape guide for model sizes, but here’s the practical summary:
| Model Size | GPU Memory Needed | Example Models |
|---|---|---|
| 7-8B params | 8-10GB | Llama 4 8B, Mistral 7B |
| 13-14B params | 12-16GB | Llama 3.1 14B, various fine-tunes |
| 30-34B params | 24-32GB | CodeLlama 34B, Mixtral |
| 70B params | 40-48GB | Llama 4 70B |
| 405B params | 80GB+ or multi-GPU | Llama 4 405B |
If you’re unsure, target running 70B parameter models comfortably. That gives you access to most open models without requiring exotic setups.
GPU Selection: The Most Critical Choice
Your GPU determines what AI work you can do. Get this right, and everything else falls into place.
The 2026 GPU Landscape for AI
NVIDIA dominates AI workloads due to CUDA ecosystem maturity. AMD is improving but still less compatible with AI frameworks. Intel’s AI GPUs are niche.
For most builders, NVIDIA is the only practical choice.
Consumer GPUs for AI
These are gaming-oriented but work well for AI inference and light training.
| GPU | VRAM | AI Performance | Street Price (approx) | Best For |
|---|---|---|---|---|
| RTX 4060 | 8GB | Entry | $300 | 7B models only |
| RTX 4070 | 12GB | Light | $500 | 7-13B models |
| RTX 4070 Ti Super | 16GB | Moderate | $750 | Up to 13B models comfortably |
| RTX 4080 Super | 16GB | Good | $1,000 | 13B models, faster |
| RTX 4090 | 24GB | Excellent | $1,800+ | Up to 30B models |
| RTX 5090 | 32GB | Outstanding | $2,500+ | 34B+ models (new 2025) |
The RTX 5090 is the new flagship for 2025/2026, offering 32GB VRAM—a significant jump from the 4090’s 24GB. If budget allows, it’s the consumer sweet spot.
For most users, the RTX 4090 or RTX 5090 offers the best balance of VRAM, performance, and availability. The 24-32GB VRAM handles most practical workloads.
See our best GPUs for running AI locally guide for deep analysis.
Professional GPUs for AI
When you need more VRAM or reliability:
| GPU | VRAM | AI Performance | Street Price (approx) | Best For |
|---|---|---|---|---|
| RTX A4000 | 16GB | Moderate | $1,000 | Professional, compact |
| RTX A5000 | 24GB | Good | $2,500 | Multi-GPU setups |
| RTX A6000 | 48GB | Excellent | $5,000 | 70B models, training |
| NVIDIA L40S | 48GB | Outstanding | $7,000+ | Enterprise workloads |
| NVIDIA H100 | 80GB | Maximum | $30,000+ | Research, large training |
For serious work beyond inference, the RTX A6000 with 48GB VRAM is the practical choice. It can run 70B parameter models without quantization.
The H100 is enterprise-grade and priced accordingly. Unless you’re doing cutting-edge research, it’s overkill.
Multi-GPU Considerations
For larger models, multiple GPUs can pool VRAM:
- Requires compatible motherboard with sufficient PCIe slots
- Need adequate power supply for multiple high-power GPUs
- Software must support multi-GPU (most AI frameworks do)
- Communication overhead affects performance
Two RTX 4090s (48GB combined) can be more cost-effective than one A6000, but with more setup complexity.
My GPU Recommendations by Budget
Under $500: RTX 4070 Ti (used) or RTX 4070—limited but functional $500-1000: RTX 4080 Super—solid entry point $1000-2000: RTX 4090—the mainstream AI powerhouse $2000-3000: RTX 5090—maximum consumer performance $3000-6000: RTX A6000—when you need 48GB $6000+: Multi-GPU setups or professional cards
CPU Recommendations: Matching to Your GPU
The CPU matters less than the GPU for most AI work, but getting the balance right prevents bottlenecks.
What the CPU Does in AI Workloads
- Data preprocessing and loading
- Model loading and memory management
- Non-GPU computation
- General system tasks
For inference workloads, even mid-range CPUs work fine. For training with heavy data processing, invest more.
Recommended CPUs by Tier
Entry Level (for inference):
- AMD Ryzen 7 7700X: 8 cores, great single-thread
- Intel Core i7-14700K: Strong all-around
Mid-Range (inference + light training):
- AMD Ryzen 9 7900X: 12 cores, excellent
- Intel Core i9-14900K: Maximum gaming crossover
High-End (training workloads):
- AMD Ryzen 9 7950X: 16 cores, outstanding
- AMD Threadripper 7960X: When you need lanes for multi-GPU
CPU Recommendations
For most AI workstations, an AMD Ryzen 9 7900X or 7950X hits the sweet spot. You get plenty of cores for data processing without the Threadripper premium.
Intel works fine but AMD currently offers better value in the high-core-count space.
PCIe Lanes Matter for Multi-GPU
If running multiple GPUs, ensure sufficient PCIe lanes:
- Consumer platforms: Limited to 1-2 GPUs at full bandwidth
- Threadripper/HEDT: 64+ lanes for multi-GPU flexibility
For single-GPU builds, any mainstream platform works.
Memory and Storage: Don’t Skimp Here
I see people overspend on GPUs while undersizing RAM and storage. Both will bottleneck your workflow if inadequate.
System RAM Requirements
System RAM is used for:
- Loading and preprocessing datasets
- Model loading before GPU transfer
- Running development environments
- General multitasking
Minimum recommendations:
| Use Case | RAM | Notes |
|---|---|---|
| Inference only | 32GB | Bare minimum |
| Development | 64GB | Comfortable |
| Training | 128GB | Recommended |
| Large datasets | 256GB+ | When data doesn’t fit |
DDR5 is standard in 2026. Get fast RAM (DDR5-5600 or better) but prioritize capacity over speed for AI workloads.
My recommendation: Start with 64GB minimum. 128GB if budget allows. RAM is easy to upgrade later.
Storage Strategy
Fast storage dramatically improves workflow, especially for training.
Primary SSD (OS + Active Projects):
- NVMe PCIe 4.0 or 5.0
- 1-2TB capacity minimum
- Read/write speeds matter for large datasets
Secondary Storage (Datasets, Models):
- Can be SATA SSD for cost savings
- 2-4TB for model weights and datasets
- LLM weights alone can be 20-100GB per model
Recommended drives:
- Samsung 990 Pro (1-2TB): Fast, reliable primary
- WD Black SN850X: Alternative primary
- Samsung 870 QVO (4TB): Cost-effective secondary
Total storage recommendation: 2TB primary NVMe + 4TB secondary minimum.
Other Components: Completing the Build
The remaining components support your GPU and CPU. Don’t let these create problems.
Power Supply (Critical!)
AI workloads are power-hungry. Undersizing the PSU causes instability.
GPU power requirements:
- RTX 4070: 200W
- RTX 4090: 450W
- RTX 5090: 575W
- RTX A6000: 300W
- Multi-GPU: Add per GPU
Total system draw calculation: GPU TDP + 150W (CPU) + 100W (other) + 20% headroom
Recommendations by build:
| GPU | Recommended PSU |
|---|---|
| RTX 4070-4080 | 750W |
| RTX 4090 | 1000W |
| RTX 5090 | 1200W |
| Multi-GPU | 1500W+ |
Get a quality 80+ Gold or Platinum PSU. Brands: Corsair, Seasonic, EVGA, be quiet!
Cooling
AI workloads run GPUs hard. Good cooling extends component life and maintains performance.
CPU cooling:
- Tower cooler: Noctua NH-D15 or similar
- AIO liquid cooler: 280mm or 360mm for high-end CPUs
Case airflow:
- Front intake, rear/top exhaust
- At least 3-4 case fans
- Consider a case with good GPU clearance
GPU cooling:
- Stock coolers usually adequate
- Ensure case can exhaust GPU heat
- Consider waterblocks for extreme builds
Motherboard
Match to your platform with features you need:
For AMD Ryzen: X670E or B650E chipset For Intel: Z790 chipset For Threadripper: TRX50 platform
Features to prioritize:
- Sufficient PCIe slots for your GPUs
- M.2 slots for NVMe drives
- Good VRM cooling for sustained loads
- USB and connectivity you need
Case
Bigger is generally better for AI workstations:
- Room for large GPUs (3-4 slot)
- Good airflow
- Space for expansion
- Open layouts help cooling
Recommendations: Fractal Design Meshify 2 XL, Corsair 5000D, be quiet! Dark Base Pro 901
Budget-Tier Builds: Complete Parts Lists
Here are complete builds for different budgets, all designed for AI workloads.
Entry Level: The “Getting Started” Build (~$1,500)
For running 7B models, experimenting with AI development.
| Component | Specific Part | Price |
|---|---|---|
| GPU | RTX 4070 12GB | $500 |
| CPU | Ryzen 7 7700X | $300 |
| Motherboard | B650E (mid-range) | $180 |
| RAM | 32GB DDR5-5600 | $100 |
| Primary SSD | 1TB NVMe Gen4 | $80 |
| Secondary SSD | 2TB SATA | $120 |
| PSU | 750W 80+ Gold | $100 |
| Case | Mid-tower w/ airflow | $80 |
| CPU Cooler | Tower cooler | $50 |
| Total | ~$1,510 |
What you can run: Llama 4 8B, Mistral 7B, small image models. Good for learning, light development, inference.
Mid-Range: The “Capable” Build (~$3,000)
For comfortable 13-30B models, fine-tuning, development work.
| Component | Specific Part | Price |
|---|---|---|
| GPU | RTX 4090 24GB | $1,800 |
| CPU | Ryzen 9 7900X | $400 |
| Motherboard | X670E (quality) | $280 |
| RAM | 64GB DDR5-5600 | $200 |
| Primary SSD | 2TB NVMe Gen4 | $150 |
| Secondary SSD | 4TB SATA | $200 |
| PSU | 1000W 80+ Gold | $150 |
| Case | Full tower w/ airflow | $150 |
| CPU Cooler | 280mm AIO | $120 |
| Total | ~$3,450 |
What you can run: Models up to 30B parameters, quantized 70B, serious development, limited fine-tuning. This is my recommended “do everything practical” build.
High-End: The “Professional” Build (~$6,000)
For 70B models, training, multi-tasking, professional work.
| Component | Specific Part | Price |
|---|---|---|
| GPU | RTX 5090 32GB or A6000 48GB | $2,500-5,000 |
| CPU | Ryzen 9 7950X | $550 |
| Motherboard | X670E (high-end) | $400 |
| RAM | 128GB DDR5-5600 | $400 |
| Primary SSD | 2TB NVMe Gen5 | $250 |
| Secondary SSD | 8TB NVMe Gen4 | $500 |
| PSU | 1200W 80+ Platinum | $250 |
| Case | Full tower premium | $250 |
| CPU Cooler | 360mm AIO | $180 |
| Total | ~$5,300-8,000 |
What you can run: Llama 4 70B comfortably, training runs, multiple concurrent models. This is serious capability.
Extreme: The “Research” Build (~$15,000+)
For 405B models, large training runs, production workloads.
| Component | Specific Part | Price |
|---|---|---|
| GPU | 2x RTX A6000 (96GB total) | $10,000 |
| CPU | Threadripper 7960X | $1,500 |
| Motherboard | TRX50 | $800 |
| RAM | 256GB DDR5 | $1,000 |
| Primary SSD | 4TB NVMe Gen5 | $500 |
| Secondary SSD | 16TB NVMe array | $1,500 |
| PSU | 1600W 80+ Platinum | $400 |
| Case | Server/workstation | $400 |
| CPU Cooler | 420mm AIO or custom loop | $300 |
| Total | ~$16,400 |
What you can run: Essentially anything. Large-scale training, 405B inference, research workloads.
Software Setup: Getting Started
Once your hardware is built, you need software. Here’s the essential stack.
Operating System
Ubuntu 22.04 LTS or 24.04 LTS is the standard for AI development:
- Best driver support for NVIDIA
- Native compatibility with AI frameworks
- Docker works smoothly
Windows works but adds friction. Most AI tools assume Linux.
NVIDIA Drivers and CUDA
Install the latest NVIDIA drivers and CUDA toolkit:
- Download from NVIDIA’s official site
- Install driver first, verify with
nvidia-smi - Install CUDA toolkit matching your driver
- Install cuDNN for deep learning
AI Frameworks
Essential installations:
- Python 3.10-3.12 via pyenv or conda
- PyTorch with CUDA support
- Transformers from Hugging Face
- vLLM for efficient inference
- Ollama for easy local model running
See our Ollama guide for the quickest path to running local models.
Docker for Reproducibility
Many AI tools distribute as Docker containers:
- Install Docker and nvidia-container-toolkit
- Enables easy environment management
- Simplifies complex dependencies
Maintenance and Longevity
A well-maintained workstation lasts years. Here’s how to keep it running.
Thermal Monitoring
Run tools like HWiNFO or GPU-Z during AI workloads. Watch for:
- GPU temperatures above 80°C (consider improving airflow)
- CPU temperatures above 90°C (check cooler mounting)
- Fan noise indicating thermal stress
Cleaning
Dust destroys thermals. Every 3-6 months:
- Blow out dust with compressed air
- Clean fan blades
- Check heatsink contact
Driver Updates
Keep NVIDIA drivers and CUDA current for:
- Performance improvements
- Bug fixes
- New feature support
Test updates before production use—occasionally new drivers introduce regressions.
Component Lifespan
Plan for eventual replacements:
- GPUs: 4-6 years typical before falling behind
- CPUs: 5-8 years typically adequate
- RAM: Very long lifespan unless defective
- SSDs: Monitor health with SMART tools
Frequently Asked Questions
How much should I spend on an AI workstation?
Start with $1,500-3,500 for a capable entry-to-mid build running most common models. $5,000-8,000 covers professional needs including 70B models. Above $10,000 is for research use cases requiring multi-GPU setups or the largest models.
Can I upgrade my existing PC for AI?
Often yes, if you have a PCIe 4.0 slot and adequate power supply. Adding an RTX 4090 to an existing gaming PC can work. Check your PSU capacity (need 1000W+ for 4090), case clearance for large GPU, and CPU won’t bottleneck significantly.
Is AMD or Intel better for AI workstations?
AMD currently offers better value for high-core-count CPUs relevant to AI work. Intel works fine but costs more at the high end. For GPUs, NVIDIA is essential—AMD GPU compute support lags significantly for AI frameworks.
Should I buy used components?
Used RTX 3090s (24GB at lower price) can be good value for GPU. Be cautious with formerly-mined cards—check for wear. CPUs and RAM are generally safe to buy used. SSDs are risky used—you can’t see wear.
What about Mac for AI?
Apple Silicon (M3/M4) handles inference well with unified memory architecture. The Mac AI guide covers this path. For training, Macs are less suitable—CUDA unavailability limits framework compatibility. For inference and development, Mac is increasingly viable.
How loud will an AI workstation be?
Under AI workloads, expect noticeable noise from GPU and CPU fans. A 4090 under load is not quiet. Good case airflow and quality fans help. Consider a different room if noise bothers you, or time intensive work for when noise is acceptable.
Conclusion
Building an AI workstation is an investment that pays dividends in capability, privacy, and long-term cost savings. Whether you start with an entry-level build and upgrade over time, or go all-in on a professional setup, you’ll have a machine specifically designed for AI work.
My key advice:
- Start with your GPU—it determines everything else
- Don’t skimp on RAM and storage—common regret points
- Plan for your actual use case, not theoretical maximum
- Budget for quality PSU and cooling—saves headaches
The mid-range build ($3,000-3,500) with an RTX 4090 handles 90% of what most practitioners need. It’s my recommended starting point unless you have specific requirements demanding more.
AI hardware evolves quickly, but a well-built workstation serves you for years. And there’s something deeply satisfying about running AI entirely on your own hardware—inference with zero latency, training without cloud costs, and complete control over your environment.
Happy building!