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.
Pays back soon. Fastest payback: RTX 4090 · 24GB in 13 mo.. At $150 a month of API spend, 8 hours a day, 0.18 per kWh.
At $150/mo API spend · 8h/day · 0.18 $/kWh
Fastest payback: RTX 4090 · 24GB in 13 mo.
- RTX 4090 · 24GB$1,599 · 550W · ~18 tok/s (70B Q4)70B needs heavy quant to fit; the previous generation, still listed by NVIDIA but scarce at retail13 moelectricity ≈ $24/mo· nets $126/mo vs API
- RTX 5090 · 32GB$1,999 · 675W · ~45 tok/s (70B Q4)runs 70B Q4 fully in VRAM; that is the list price, and cards at it sell out17 moelectricity ≈ $29/mo· nets $121/mo vs API
- Mac Studio · M5 Max$2,499 · 110W · ~20 tok/s (70B Q4)36GB to start, so 70B Q4 needs the 64GB build; up to 128GB, near-silent, low draw · that build is $3,49917 moelectricity ≈ $5/mo· nets $145/mo vs API
- Mac Studio · M5 Ultra$5,499 · 130W · ~41 tok/s (70B Q4)96GB to start; up to 512GB from late October, which runs models no single GPU can fit3.2 yrelectricity ≈ $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 sources, dated in the strip below: 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), so 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 $1,599, an RTX 5090 $1,999 at list, a Mac Studio from $2,499); after that you mostly pay electricity, and less than most people expect: running the 4090 8 hours a day at the US-average $0.18/kWh comes to about $24 a month. So at $150 a month of API spend the 4090 pays for itself in about 13 months. Below roughly $113 a month it stops clearing the 18-month bar this calculator calls a fast payback, and once your bill falls under what the box costs in electricity there is no payback at all. Enter your own numbers above to see your break-even.
When does an RTX 5090 pay for itself for local AI?
Take the price you can actually pay. NVIDIA lists it at $1,999, which is what this calculator uses, but cards at that figure sell out and street listings have ranged $2,000–$5,000+ through the 2026 memory shortage, so treat the answer below as the best case and re-run it with what you are quoted. From that price, subtract the $29 a month of electricity it draws at 8 hours a day and $0.18/kWh, and divide by your monthly API spend. At $200 a month of API usage that is about a 12-month payback; at $400 a month, about 5 months. Below about $140 a month it stops paying back inside 18 months, and below the $29 it costs to run there is no payback at all. The 5090 runs Llama 3.3 70B fully in its 32GB VRAM at about 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 ($1,599) is the cheapest entry, but 70B models need heavy quantization to fit 24GB. The RTX 5090 ($1,999) runs 70B Q4 fully in 32GB VRAM at about 45 tok/s, the fastest box here. A Mac Studio (from $2,499, or $3,499 configured with the memory these speeds assume, up to 512GB unified memory) is slower at 20–41 tok/s but near-silent and sips power, 110–130W against 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, 110–130W for a Mac Studio) by hours used per day, by 30, by your rate. A 5090 run 8 hours a day at $0.18/kWh is about $29 a month; a Mac Studio is closer to $5–$6. Idle draw is far lower, so the cost is dominated by the hours you are actively generating tokens.