How Much Does It Cost to Run LLMs Locally? Hardware, Electricity vs API Over 3 Years (2026)

What are the cost items of a local setup?

Four: the GPU, the rest of the build, electricity and depreciation, with the GPU over 60% of the total.

• GPU: used prices come from the live data on this site. This guide uses three tiers as examples: the RTX 3090 (entry 24GB), the RTX 4090 (mainstream) and the RTX PRO 6000 Blackwell (96GB high end), plus a Mac Studio M4 Max 128GB as a whole-machine comparison.
• Rest of the build: CPU, board, 32GB of RAM, an 850W PSU and a case run about $700 in 2026. Expert offload for MoE models wants 128GB of RAM, another $300 or so. If you already own a desktop, this line is zero.
• Electricity: rated card power × real load factor × hours × price per kWh. Inference typically draws 50% to 70% of the rating; we use 60%, 8 hours a day and residential rates.
• Depreciation: a card is worth 30% to 40% of its price after three years, and old cards like the 3090 fall slowly. The table below does not subtract resale value, so real cost is 10% to 20% lower.

Three-year total for three builds

An entry 3090 build is about $2,700 over three years, a 4090 build about $4,300, and an RTX PRO 6000 build over $18,000. Card prices are the current used medians:

The Mac Studio is a complete machine, so "rest of the build" is zero. Its 480W rating overstates inference draw, which is closer to 100W, so its electricity line is high; it does not change where it lands in the table.

What does the same usage cost on the API?

API prices spread far wider than hardware: the same million output tokens cost $0.28 from DeepSeek V4 Flash and $10 from Claude Sonnet 5, a 35x gap. September 2026 list prices per million tokens (input / output):

Assuming an even input/output split, three years of API at three typical volumes:

Where is the break-even?

Break-even tokens per day = local three-year cost ÷ API price per token ÷ 1,095 days. With the 3090 build at $2,700:

• Against DeepSeek V4 Flash (blended $0.21 per million): 11.7 million tokens a day. A 3090 runs a 32B model at 4-bit near 32 tokens/s, about 900K tokens in 8 hours. It never catches up.
• Against GPT-5.6 luna (blended $0.70 per million): 3.5 million tokens a day, also beyond one card.
• Against Claude Sonnet 5 (blended $6 per million): about 410K tokens a day, four hours of generation. Heavy users get there.

Mind the premise of the third line: you must accept a 32B-class local model in place of Sonnet 5. In most workloads that is a capability step down, and the savings get paid back in rework time.

When is local clearly worth it?

Four cases need no spreadsheet: data that cannot leave the machine, offline operation, fine-tuning and large batch jobs.

1. Data cannot leave the machine: legal documents, medical records, unreleased code, financial data. One compliance incident costs more than the card.
2. Offline: workshops, ships, air-gapped offices.
3. Fine-tuning: a LoRA run on an 8B to 14B model finishes in hours on a 24GB card. API fine-tuning bills per token and never hands you the weights.
4. Batch: classifying, embedding or summarizing hundreds of thousands of documents runs overnight on one 3090, where an API means bills and rate limits.
5. You were buying a gaming card anyway: the card is paid for, the marginal cost is electricity, and local inference is nearly free.

When should you not buy a card?

Personal use under about 10K tokens a day, people who only want the strongest models, and people without a desktop should not buy a GPU for LLMs.

• A few dozen chat turns and some emails a day cost less per year on the API than the card's electricity.
• If you want Sonnet 5 or V4 Pro-level ability, there is no local equivalent; a card does not solve it.
• Without a desktop the one-time outlay doubles. For the Mac route, see Mac unified memory vs a discrete GPU.
• Budget-limited but decided: start with the cheapest card at every VRAM tier.