AI Workstation Guide
Build your ultimate local AI machine
- GPU is 50-60% of budget - it's the most important component
- RTX 4090 24GB is the best consumer GPU for AI
- Multi-GPU viable for larger models but adds complexity
- 64GB+ system RAM recommended for AI workloads
- Power and cooling for sustained loads require planning
Planning Your Build
Define your requirements before buying components.
Use Case Assessment
Inference only: Consumer GPUs sufficient. Training: Need more VRAM, consider professional cards. Mixed workloads: Balance capability vs cost.
Model Size Targets
7B-13B models: Single 12GB card. 32B models: Single 16GB card. 70B models: Single 24GB or dual 12GB. 405B+ models: Multi-card professional setup.
Budget Allocation
50-60% on GPU(s). 15-20% on CPU/motherboard/RAM. 10-15% on storage. 10-15% on case/cooling/PSU. GPU is the priority.
Component Selection
Choosing the right parts for an AI workstation.
GPU
NVIDIA for CUDA compatibility. RTX 4090 24GB is the consumer king. RTX 3090 used offers great value. Multi-GPU: PCIe lanes matter.
CPU
Not critical for inference (GPU-bound). More cores help with data loading. AMD Ryzen 7/9 or Intel i7/i9. PCIe 5.0 for future GPUs.
Motherboard
Check PCIe lane configuration for multi-GPU. ATX or E-ATX for space. Good VRM for stable power delivery.
RAM
32GB minimum, 64GB recommended. 128GB+ for CPU offloading large models. DDR5 if available, not critical for GPU inference.
Storage
NVMe SSD for model storage. 2TB minimum, 4TB+ for model collections. Fast loading improves workflow.
RTX 4090
Multi-GPU Considerations
Running multiple GPUs for larger models or faster inference.
When to Use Multiple GPUs
Single 4090 can't run 70B at Q8: Use dual 4090 or 3090. Need faster batch inference: Parallel GPUs help. Training: Multi-GPU often required.
PCIe Lane Distribution
Consumer platforms: 16+4 or 8+8 for 2 GPUs. HEDT (Threadripper): 64+ lanes. Server (EPYC): 128+ lanes. x8 vs x16 matters less for inference.
Spacing and Cooling
Triple-slot cards need space. Consider water cooling. Blower cards help in tight spaces. Plan airflow carefully.
Software Support
llama.cpp: Excellent multi-GPU via tensor parallelism. vLLM: Good multi-GPU for serving. Training frameworks: Native multi-GPU.
Cooling & Power
AI workloads are sustained high-power. Plan accordingly.
Power Supply
RTX 4090: 450W each, 850W PSU minimum for one, 1200W+ for two. Quality matters: 80+ Gold or Platinum. ATX 3.0 for native 12VHPWR.
CPU Cooling
Quality air cooler or 240mm+ AIO. Not critical for GPU-heavy workloads but sustained loads need decent cooling.
Case Airflow
Good front intake, rear/top exhaust. Consider open-frame for multi-GPU. Temperature monitoring important.
Ambient Temperature
Keep room cool for sustained workloads. AC or good ventilation helps. GPU thermal throttling reduces performance.
RTX 4090
Example Builds
Reference configurations at different budgets.
Starter AI Workstation ($1,500)
RTX 4070 Ti Super 16GB + Ryzen 7 7800X3D + 64GB DDR5 + 2TB NVMe + 850W PSU. Runs 32B models, fast inference, entry AI development.
Enthusiast AI Workstation ($3,000)
RTX 4090 24GB + Ryzen 9 7950X + 128GB DDR5 + 4TB NVMe + 1000W PSU. Runs 70B models at Q4, serious AI work, some training possible.
Professional AI Workstation ($8,000)
Dual RTX 4090 + Threadripper 7960X + 256GB DDR5 + 8TB NVMe + 1600W PSU. 48GB combined VRAM, 70B at higher quant, production-ready.
Research/Enterprise ($20,000+)
RTX 6000 Ada 48GB or A100 80GB + EPYC + 512GB+ ECC RAM. Full model training, enterprise deployment, maximum capability.
RTX 4070 Ti Super
RTX 4090
RTX 6000 Ada
NVIDIA A100 80GB
Frequently Asked Questions
Related Guides & Resources
Ready to Get Started?
Check our step-by-step setup guides and GPU recommendations.