Free tool
Build vs Buy: Local-AI Payback Calculator
Enter your monthly AI API spend and see when buying a GPU or Mac to run models locally pays for itself — across the RTX 4090, RTX 5090, and Mac Studio, with electricity and real throughput factored in. The number that actually decides build-vs-buy.
At $150/mo API spend · 8h/day · 0.18 $/kWh
Fastest payback: Mac Studio · M4 Max in 14 mo.
- Mac Studio · M4 Max$1,999 · 110W · ~18 tok/s (70B Q4) · up to 128GB unified; near-silent, low draw14 moelectricity ≈ $5/mo· nets $145/mo vs API
- RTX 4090 · 24GB$1,800 · 550W · ~18 tok/s (70B Q4) · 70B needs heavy quant to fit14 moelectricity ≈ $24/mo· nets $126/mo vs API
- RTX 5090 · 32GB$2,900 · 675W · ~45 tok/s (70B Q4) · runs 70B Q4 fully in VRAM24 moelectricity ≈ $29/mo· nets $121/mo vs API
- Mac Studio · M3 Ultra$3,999 · 130W · ~28 tok/s (70B Q4) · up to 256GB; runs models a GPU can't fit28 moelectricity ≈ $6/mo· nets $144/mo vs API
Going local? Check it'll actually run your model first.
Payback = hardware price ÷ (your monthly API spend − local electricity). Electricity = power draw × hours/day × 30 × your rate. Data web-verified June 2026: GPU/Mac prices (NVIDIA MSRP + street trackers; Apple), sustained-inference power draw, and Llama 3.3 70B Q4 single-stream throughput. GPU street prices swing hard(RTX 5090 has run $2k–$5k+ in the 2026 memory shortage) — treat the defaults as a starting point. Payback isn't “free”: open 70B models match frontier models on many tasks but still trail on the hardest reasoning, you have to actually use the box enough, and resale/PSU/cooling are ignored. A decision aid, not a quote.
Frequently asked
- Is it cheaper to run AI locally or keep paying for an API?
- It depends almost entirely on how much you spend now. The hardware is a one-time cost (an RTX 4090 ~$1,800, an RTX 5090 ~$2,900 street, a Mac Studio from ~$2,000); after that you mostly pay electricity — running a ~575W GPU 8 hours a day at the US-average ~$0.18/kWh is only about $25/month. So if your API bill is ~$150/month, a $1,800 GPU pays for itself in roughly 14 months; at $20/month it never pays back. Enter your own numbers above to see your break-even.
- When does an RTX 5090 pay for itself for local AI?
- Take its street price (~$2,900 in the 2026 memory shortage, though it has ranged $2,000–$5,000+), subtract the ~$30/month electricity to run it, and divide by your monthly API spend. At $200/month of API usage that's about a 17-month payback; at $400/month, roughly 8 months; under ~$60/month it effectively never pays back. The 5090 runs Llama 3.3 70B fully in its 32GB VRAM at ~45 tok/s, so it genuinely replaces a frontier-class API for many tasks.
- Do you actually save money running LLMs locally?
- Only if you use it enough to clear the hardware cost, and only for work an open model handles well. Open 70B models (Llama 3.3, Qwen 3, DeepSeek) now match GPT and Claude on a lot of coding and writing, but still trail the frontier on the hardest reasoning — so heavy, steady users save real money while light or reasoning-critical users are usually better off on the API. The calculator shows the monthly spend you'd need to break even.
- RTX 4090 vs RTX 5090 vs Mac Studio — best value for local LLMs?
- The RTX 4090 (~$1,800) is the cheapest entry, but 70B models need heavy quantization to fit 24GB. The RTX 5090 (~$2,900) runs 70B Q4 fully in 32GB VRAM at ~45 tok/s — the fastest. A Mac Studio (from ~$2,000, up to 256GB unified memory) is slower (~15–28 tok/s) but near-silent, sips power (~100W vs 550–675W for the GPUs), and its huge memory runs models a single GPU can't fit. The fastest payback usually goes to whichever box you'll actually keep busy.
- How much does electricity add to running a local LLM?
- Less than most people expect. Multiply power draw (≈550W for a 4090, ≈675W for a 5090 including the host PC, ~100W for a Mac Studio) by hours used per day, by 30, by your rate. A 5090 run 8h/day at $0.18/kWh is about $29/month; a Mac Studio is closer to $4. Idle draw is far lower, so the cost is dominated by the hours you're actively generating tokens.