TL;DR: Budget local AI build (runs 7B–14B models): $700–900. Mid-range build (runs up to 30B models): $1,800–2,400. High-end build (runs anything): $4,000–5,500. The GPU is 50–60% of the total cost at every tier. Everything else is secondary.
Building a local AI rig in 2026 is more straightforward than it looks. The GPU determines your model capability. Everything else — CPU, RAM, storage — just needs to be "good enough" to not bottleneck the GPU.
This estimator breaks down real component costs at three build tiers based on March 2026 prices.
How to Use This Guide
Pick your primary use case, match it to a tier, then use the component list as a shopping baseline. Prices are approximate based on US retail (Newegg, Amazon, B&H) and eBay sold listings for used parts.
VRAM determines which models you can run. Everything else in the system just needs to keep up with the GPU.
Tier 1: Budget Build — Runs 7B–14B Models
Target use case: Daily AI assistant, coding help, writing, Q&A. Not for running 30B+ models.
Total estimated cost: $700–950
GPU — RTX 4060 Ti 16GB (used) or Arc B580 (new)
- RTX 4060 Ti 16GB used: $330–380
- Intel Arc B580 new: $249–299
- The 4060 Ti is the better choice if you want solid 14B model speed. The Arc B580 is better if you're on a strict budget. Skip the RTX 4060 8GB — 8GB VRAM caps your model options too severely.
- GPU budget: $250–380
CPU — AMD Ryzen 5 5600 or Intel Core i5-12400 (used)
- Ryzen 5 5600 used: $70–90
- Intel i5-12400 used: $80–100
- For LLM inference, the CPU does minimal work when the GPU handles everything. Any modern 6-core CPU is fine. Don't spend more than $100 here.
- CPU budget: $70–100
Motherboard — B550 (AMD) or B660 (Intel)
- B550 motherboard used: $60–90
- B660 motherboard new budget: $80–110
- Match the socket to your CPU. No special features needed — just PCIe x16 for the GPU and DDR4/DDR5 support.
- Motherboard budget: $60–110
RAM — 32GB DDR4 (2x16GB)
- 32GB DDR4 3200 kit new: $40–60
- 32GB minimum for comfortable AI use. 16GB is technically enough for 7B models but creates pressure when running background tasks. 32GB gives you room to use long context windows that overflow to system RAM.
- RAM budget: $40–60
Storage — 1TB NVMe SSD
- 1TB NVMe new: $50–70
- Models are large files (5–20GB each). A 1TB drive holds 5–10 models with room for OS and other software. If you plan to collect many models, buy 2TB.
- Storage budget: $50–70
PSU — 650W 80+ Bronze
- 650W Bronze new: $50–70
- The Arc B580 and RTX 4060 Ti don't need huge power. A quality 650W unit handles the whole system. Don't buy cheap no-name PSUs — PSU failures can damage other components.
- PSU budget: $50–70
Case — Budget mid-tower
- Budget mid-tower new: $40–70
- No requirements beyond fitting a standard ATX board and your GPU's length. Check GPU dimensions — some AIB RTX 4060 Ti cards are 300mm+ long.
- Case budget: $40–70
Total Tier 1 estimate:
- With Arc B580 (new): $609–809
- With RTX 4060 Ti 16GB (used): $690–860
Tier 2: Mid-Range Build — Runs 7B–30B Models
Target use case: Professional daily AI use, coding assistant, 24B–30B model capability, comfortable 16GB VRAM.
Total estimated cost: $1,800–2,400
GPU — RTX 5070 Ti (new) or RTX 3090 (used)
- RTX 5070 Ti new: $800–950 (targeting near MSRP)
- RTX 3090 used: $720–850
- The choice depends on what you value. The 5070 Ti is faster (896 GB/s bandwidth) but has 16GB VRAM. The 3090 is slower but has 24GB — meaning better 30B model support at higher quantizations. For 7B–14B primary use with occasional 30B work: 5070 Ti. For 30B as a regular model: 3090.
- GPU budget: $720–950
CPU — AMD Ryzen 7 5800X3D or Intel Core i7-13700 (used)
- Ryzen 7 5800X3D used: $180–220
- i7-13700 used: $200–240
- The 3D V-Cache on the 5800X3D benefits CPU inference if you ever offload model layers. If you're GPU-only, the i7-13700 is fine. Both are overkill for pure GPU inference but give headroom.
- CPU budget: $180–240
Motherboard — X570 (AMD) or Z690 (Intel)
- X570 used: $90–130
- Z690 used: $110–150
- PCIe 4.0 support ensures no slot-speed bottleneck with the 5070 Ti. B550 works fine too if the 5070 Ti fits the slot.
- Motherboard budget: $90–150
RAM — 64GB DDR4 (2x32GB)
- 64GB DDR4 3200 kit: $80–110
- Step up to 64GB at this tier. Long-context work (32K–128K tokens) can push KV cache overflow into system RAM. 64GB means you won't hit that ceiling under normal use.
- RAM budget: $80–110
Storage — 2TB NVMe SSD
- 2TB NVMe new: $80–120
- At this tier you'll accumulate models. 2TB accommodates 10–20+ models comfortably.
- Storage budget: $80–120
PSU — 850W 80+ Gold
- 850W Gold new: $80–110
- The 3090 draws up to 350W and the 5070 Ti can peak near 350W. A quality 850W unit handles either with headroom for the rest of the system.
- PSU budget: $80–110
Case — Mid-tower with good airflow
- Quality mid-tower: $70–100
- At this tier the GPU runs hotter and benefits from better airflow. A mesh-front case makes a real difference for sustained inference under load.
- Case budget: $70–100
CPU Cooler — 240mm AIO or quality tower cooler
- 240mm AIO: $60–90
- Tower cooler (Scythe Fuma 3, Noctua U12S): $50–70
- Ryzen 7 / i7 processors get warm under sustained system load. Box coolers are undersized. Get proper cooling.
- Cooler budget: $50–90
Total Tier 2 estimate:
- With RTX 3090 (used): $1,450–1,870
- With RTX 5070 Ti (new): $1,530–1,970
Tier 3: High-End Build — Runs Anything
Target use case: 70B model inference (with CPU offloading or dual GPU), large context windows, professional/commercial workloads, maximum performance.
Total estimated cost: $4,000–5,500
GPU — RTX 4090 (used) or RTX 5090 (new at MSRP)
- RTX 4090 used: $2,100–2,400 (used market prices as of March 2026; prices fluctuate — check eBay completed listings for current values)
- RTX 5090 new at MSRP: $1,999 (street price currently much higher)
- The 4090's 24GB GDDR6X and 1,008 GB/s bandwidth is still the best-value high-end consumer card for local AI. The 5090's 32GB GDDR7 and 1,792 GB/s is faster and fits larger models — if you can get it at or near MSRP.
- GPU budget: $2,000–2,400
CPU — AMD Ryzen 9 7950X or Intel Core i9-13900K
- Ryzen 9 7950X: $350–420
- i9-13900K used: $320–380
- At this tier, CPU performance matters more. Partial CPU offloading for 70B models means the CPU actually processes model layers. 16 high-performance cores handle offloaded layers significantly faster than 6–8 core budget chips.
- CPU budget: $320–420
Motherboard — X670E (AMD) or Z790 (Intel)
- X670E: $200–300
- Z790: $180–280
- PCIe 5.0 for maximum GPU bandwidth. NVMe 5.0 support if you want the fastest possible model load times. Feature-rich enough to support future upgrades.
- Motherboard budget: $180–300
RAM — 128GB DDR5 (2x64GB)
- 128GB DDR5 5600: $180–240
- At this tier you may run 70B models with CPU offloading, which requires the KV cache and offloaded layers to live in system RAM. 128GB ensures nothing bottlenecks. DDR5 bandwidth also benefits the CPU offloading speed.
- RAM budget: $180–240
Storage — 4TB NVMe SSD (or 2TB + 4TB HDD)
- 4TB NVMe: $200–280
- Or: 2TB NVMe ($90) + 4TB HDD ($70) = ~$160 for model cold storage
- Power users collect many large models. 4TB ensures you're never deleting models to make room.
- Storage budget: $160–280
PSU — 1000W 80+ Platinum
- 1000W Platinum: $130–170
- The 4090 peaks at 450W, 5090 at 575W. Full system under load can hit 700–800W. A quality 1000W PSU runs the system with efficiency headroom.
- PSU budget: $130–170
Case — Full-tower with top-tier airflow
- Full-tower or large mid-tower: $120–200
- 4090/5090 cards are long (330–340mm) and hot. Cases with 360mm AIO support, multiple fan slots, and excellent airflow matter at this tier.
- Case budget: $120–200
Cooling — 360mm AIO
- 360mm AIO: $100–150
- Ryzen 9 and i9 chips push serious heat under sustained workloads. A 360mm AIO keeps them throttle-free.
- Cooler budget: $100–150
Total Tier 3 estimate:
- With RTX 4090 (used): $3,290–4,160
- With RTX 5090 (new at MSRP): $3,190–3,960
Note: current RTX 5090 street price is well above MSRP. At $4,000 street price, the 5090 build pushes toward $5,200–5,500.
What You Can Skip
RGB: Adds cost, no performance benefit.
NVMe Gen 5: Model load times are faster, but Gen 4 is adequate for most workflows. Save the money.
Overclocking-focused motherboard: For inference workloads, overclocking provides minimal benefit. B550/Z690 is fine.
4K monitor: Nice to have, completely irrelevant to model performance.
The Single Best Value Move at Any Tier
Allocate as much budget as possible to the GPU. A mid-range CPU with a high-end GPU outperforms a high-end CPU with a mid-range GPU for local AI inference every time. The GPU is the engine; everything else is plumbing.