Every "buy a GPU and save on API bills" thread skips the actual arithmetic: what a token really costs once you own the hardware, and how many tokens it takes to repay the card. This post works the math end to end with every assumption stated — so you can swap in your own numbers and see your own break-even point.
What does local inference actually cost per million tokens?
Worked example, assumptions first: an RTX 4060 Ti 16GB ($499 launch MSRP per CompareAIHardware's GPU database) running an 8B model at Q4 quantization at about 95 tokens per second (TechPowerUp measurement in CompareAIHardware's benchmark database), drawing roughly 70% of its 160 W board power under sustained inference — about 112 W, an assumption — at $0.15 per kWh, an assumed rate; your bill differs.
The arithmetic: one million output tokens at 95 tok/s takes 1,000,000 ÷ 95 ≈ 10,526 seconds ≈ 2.93 hours. Energy: 2.93 h × 0.112 kW ≈ 0.33 kWh, which costs 0.33 × $0.15 ≈ $0.05 per million output tokens. In a $0.30/kWh market (typical of Germany), the same tokens cost about $0.10. That is electricity only — the $499 hardware is amortized separately, and that amortization is where the break-even lives. Note what is absent: any claim that this is a benchmark. It is a worked example, and the utilization assumption is the honest variable the whole calculation swings on.
What do API providers charge?
Per DeepSeek's published pricing page (checked August 2026), deepseek-v4-flash bills $0.22 per 1M input tokens on cache miss and $0.66 per 1M output tokens at off-peak rates, with peak hours priced at exactly double; cached input drops to $0.007 per 1M. Premium closed APIs cost multiples of that — we deliberately cite no specific figure for them, since tiers change and this post's job is the method, not the price list. For a blended worked example, assume a 30% input / 70% output mix at off-peak: (0.3 × $0.22) + (0.7 × $0.66) ≈ $0.53 per 1M tokens. Your workload's mix moves this number; long-context workloads with heavy cached input move it a lot.
Where is the break-even point?
CompareAIHardware's cloud vs local AI cost comparison deliberately publishes no break-even multiplier — electricity rates, utilization, and street prices differ per user, so the page records the facts (rental rates, MSRPs, board power) and leaves the multiplication to your usage profile. Respect that framing: what follows is a worked example, not a verdict. Savings per 1M tokens: $0.53 − $0.05 ≈ $0.48. Hardware to repay: $499. Break-even: 499 ÷ 0.48 ≈ 1.04 billion tokens. Now the utilization question that decides everything:
| Monthly volume (worked example) | API cost @ $0.53/1M | Local electricity @ $0.05/1M | Months to repay $499 GPU |
|---|---|---|---|
| 10M tokens | $5.28 | $0.49 | ~104 (never, realistically) |
| 100M tokens | $52.80 | $4.90 | ~10.4 |
| 500M tokens | $264.00 | $24.50 | ~2.1 |
There is a third path the per-token framing hides: renting GPUs by the hour. CompareAIHardware's cloud instance database records a RunPod RTX 3090 at $0.22 per hour on-demand ($0.10 spot) and a single RTX 4090 at $0.34 — recorded rates for bursty workloads that need real capacity occasionally without owning anything.
When does cloud still win?
The comparison guide's list is blunt, and the break-even math backs it. Cloud wins for sporadic use (hourly billing, no idle hardware), bursty team demand (rent an eight-GPU node for a day, then release it), and frontier-model access — hosted endpoints serve models no consumer GPU can hold, and the gap is widening: our guide to what DeepSeek V4 Flash needs to run locally shows frontier-class memory requirements climbing past single consumer cards. Cloud also wins on zero maintenance — no drivers, cooling, or failed fans. Local wins on privacy, unlimited usage without meter-watching, and sustained daily workloads where the hardware earns its price every month. If you go local, buy well: our used RTX 3090 inspection guide covers the secondhand route that stretches the break-even math in your favor.
Frequently asked questions
Does the math change for bigger models?
The structure holds; the numbers move. Larger models need more VRAM (frontier-class models exceed single consumer cards entirely), often run slower per token, and can demand costlier hardware — all of which pushes break-even further out unless your volume is genuinely high.
What if my electricity is expensive?
At $0.30/kWh the worked example's per-token cost roughly doubles to about $0.10 per 1M output tokens. That is still far below the API anchor, but it stretches the break-even proportionally — the sensitivity is linear, which is why the assumption must be stated.
Is renting a GPU by the hour a middle path?
Yes. At recorded rates like $0.22 per hour for an RTX 3090, a bursty workload that needs real capacity a few days a month rents instead of buys — no maintenance, no idle capital, and spot rates lower still for batch-tolerant jobs.
Why won't the comparison guide just publish a break-even multiplier?
Because one multiplier would be wrong for most readers: electricity rates, utilization, and street prices all vary. The guide publishes recorded rates, MSRPs, and board power — the inputs — and leaves the arithmetic to your usage profile, which is exactly the habit this post applies.
Sources and further reading
- Cloud vs Local AI on Compare AI Hardware (linked above) — cloud instance database rental rates (RunPod, Lambda, Vast.ai, AWS, GCP, Azure), GPU launch MSRPs and board power from its GPU database; updated August 14, 2026
- DeepSeek Models & Pricing page (checked August 2026): deepseek-v4-flash $0.22/1M input (cache miss, off-peak), $0.66/1M output off-peak, peak = 2× off-peak, cached input $0.007/1M — api-docs.deepseek.com/quick_start/pricing
- 8B throughput reference: ~95 tokens/second on Llama-3-8B Q4 (TechPowerUp measurement in CompareAIHardware's benchmark database, via its Best GPU for LLMs guide)
Disclosure: machine-learning.null.pictures and compareaihardware.com are operated by the same team. Links to compareaihardware.com are editorial recommendations, not paid placements.
For a fuller version of this math, the cloud vs local AI cost comparison includes break-even tables by usage level.
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