Written by Jakub Rusinowski · Last updated 2026-07-08 · Prices verified 2026-03-01
For heavy daily use (about 2 million tokens per day), running an open-weight model on a used RTX 3090 build costs roughly $76/month all-in (24-month hardware amortization plus electricity), while the same volume on GPT-4o costs about $285/month — so the hardware pays for itself in around 5 months. At light usage (under ~200k tokens/day) cloud APIs stay cheaper, because the hardware never earns back its up-front cost.
The cost dimension, side by side — what each dollar actually buys you.
| Dimension | Local | Cloud |
|---|---|---|
| Up-front cost | $500–2,900 for a capable build (used RTX 3090 tier: ~$1,240) | $0 — pay as you go |
| Marginal cost per 1M tokens | Electricity only: ~$0.30–0.60 at $0.15/kWh | $0.18 (hosted Llama 8B) to $15+ (frontier output tokens) |
| Cost predictability | Flat — usage spikes cost nothing extra | Linear with usage; spikes show up on the invoice |
| Model quality ceiling | 70B-class open models (24–48 GB VRAM) | Frontier models (GPT-4o, Claude, Gemini Pro) |
| Depreciation / lock-in | Hardware depreciates; GPUs hold resale value unusually well | No asset, no resale — spend is gone |
| Hidden costs | Your setup time; occasional maintenance | Rate-limit engineering, egress, retries, price changes |
Heavy daily use: ~2M tokens/day (a busy assistant, batch jobs, or a small product) — 2M tokens/day, Used RTX 3090 24GB Value King vs GPT-4o (OpenAI), electricity $0.15/kWh. Adjust every input in the interactive calculator on this page.
| Cloud cost / month | $285 (GPT-4o, $2.5/M input + $10/M output) |
| Local cost / month (24-mo TCO) | $76.11 — $24.44 electricity + hardware amortization |
| Hardware up-front | $1,240 (Used RTX 3090 24GB Value King) |
| Break-even | Month 5 — cumulative cloud spend passes local |
Estimates: 70/30 input/output mix, 24-month amortization, no resale value, load-time electricity only. Cloud prices last verified: 2026-03-01. Hardware street price checked: 2026-07-06.
Every local-vs-cloud cost argument reduces to three numbers: your daily token volume, the per-token price of the cloud model you would otherwise use, and the up-front price of hardware capable of a comparable job. Everything else — electricity, amortization period, input/output mix — moves the answer by weeks, not months.
Cloud pricing is public and precise. As of our last verification, OpenAI charges $2.50 per million input tokens and $10.00 per million output tokens for GPT-4o, Anthropic charges $3.00/$15.00 for Claude Sonnet, and hosted open models are dramatically cheaper — Together.ai serves Llama 3.1 8B at $0.18 per million tokens in either direction. That spread matters: the honest cloud comparison for a local 8B model is the $0.18 hosted version of the *same model*, not a frontier model it can't match.
On the local side, the dominant cost is the GPU. A used RTX 3090 with 24 GB of VRAM — still the best value in local AI, as our GPU buyer's guide argues — anchors a complete build at about $1,240 (street prices, checked July 2026, see /build). That machine runs 30B-class models at Q4 quantization: genuinely useful quality, a tier below frontier.
Take a heavy user: 2 million tokens a day at a 70/30 input/output split.
Break-even: $1,240 ÷ ($285 − $24) ≈ 4.8 months. After that, the marginal cost of every additional token is electricity — roughly $0.40 per million tokens, an order of magnitude below even budget cloud tiers.
Now the honest inversions. Drop the volume to 200k tokens/day and the cloud bill falls to ~$28/month; break-even stretches past three and a half years — longer than you should plan around a used GPU. Or keep the volume but switch the cloud comparison to GPT-4o Mini ($0.15/$0.60): the cloud bill is ~$17/month and local never breaks even on cost alone. If Mini-class quality genuinely covers your workload, cloud wins the pure cost argument at almost any volume.
The calculator below uses the same math as this page — one shared module, not marketing arithmetic. Assumptions: 24-month amortization, zero resale value (conservative — 3090s resell well), electricity billed only for load time, and no cloud volume discounts. It ignores things that are real but unquantifiable per reader: your time to set up (an evening with Ollama, realistically), the value of offline capability, and the quality gap between a 30B open model and a frontier API — which for many workloads (summarization, extraction, internal chat, RAG over your own docs) is smaller than the price gap implies. See our /cost calculator for per-model comparisons across the whole library.
The trap in every cost comparison is holding quality constant when it isn't. A local Qwen 3 32B is not GPT-4o. But the right question is whether it clears *your* quality bar. If it does, you're comparing $0.40/M tokens against $4.75/M blended — and local wins by 10×. If it doesn't, no electricity math rescues a model that can't do the job; pay for the API. Run the model first (rent an hour — see how to use RunPod — or check what your GPU can run) before buying anything.
Cost favors local surprisingly early for heavy users — a used RTX 3090 build recoups itself in about 5 months against GPT-4o at 2M tokens/day — but the comparison flips completely for light users or Mini-class workloads, where cloud is cheaper essentially forever. Be honest about your real daily volume and the cheapest cloud model that actually meets your quality bar; then the calculator gives you a date, not an opinion.
Not sure which tier fits? The build recommender maps budgets to complete part lists — or check what your existing GPU already runs for free.
Methodology & assumptions. All cost figures are estimates from one shared model (lib/costCompare.ts): cloud costs = tokens/day × published per-1M-token prices at a 70/30 input/output split × 30 days; local costs = hardware amortized over 24 months with no resale value + electricity for load time only at your rate, with machine count scaled when volume exceeds one machine’s throughput. Cloud prices carry per-entry source URLs and verification dates; hardware prices come from the curated /build catalog (street prices with check dates). Real bills vary with usage mix, discounts, and idle power — treat break-even months as directional, not contractual.