# Can a Local Model Replace Your Paid AI Subscription\?

> A practical cost comparison of already\-owned hardware\, an \$820 GPU upgrade\, and a \$2\,300 dedicated computer\, with local model benchmarks\, explicit power assumptions\, and subscription break\-even math\.

An $820 graphics-card upgrade would need about 42 months to pay for itself by replacing a $20 monthly AI subscription. A $2,300 computer would need roughly ten years. Use a suitable machine you already own, and there is no new hardware bill to recover.

Replace $100 a month instead, and those first two payback periods fall to about eight and 23 months.

Those numbers are calculated examples, with the assumptions below. They expose the two questions that matter most: how much new hardware do you need, and how much of your existing AI bill can you actually stop paying?

## Three ways to run the numbers

We'll compare an existing computer, a GPU upgrade, and a dedicated machine. The $820 and $2,300 figures are round scenario budgets, not live retail quotes or recommendations to buy a particular configuration. The upgrade assumes a compatible desktop is already available; a new power supply or other required parts belong in its upfront cost.

For processing speed, we use published community measurements from [llm-bench.io's gpt-oss-20b runs](https://llm-bench.io/models/gpt-oss-20b) and its [Qwen3-Coder run on an M5 Pro](https://llm-bench.io/benchmarks/cmty2owsr00bt01pd0dkl76q3). These were not tests we ran ourselves.

| Cost scenario | Hardware and model behind the speed estimate | Input processing | Output generation |
|---|---|---:|---:|
| Already owned: $0 new hardware | M4, 32 GB; gpt-oss-20b, MXFP4 | 402.3 tokens/s | 25.3 tokens/s |
| Upgrade budget: $820 | RX 9070 family, 16 GB; gpt-oss-20b, MXFP4_MOE | 4,242.1 tokens/s | 132.4 tokens/s |
| Dedicated computer budget: $2,300 | M5 Pro, 48 GB; Qwen3-Coder-30B, Q4_K_M | 1,449.9 tokens/s | 82.9 tokens/s |

The GPU submission groups the RX 9070, 9070 XT, and 9070 GRE under one hardware label. It doesn't identify the exact card. Its speed is therefore a family-level proxy for the upgrade scenario, not a benchmark of a specific $820 purchase.

The runs also used different configured context windows: 8,192 tokens on the M4, 98,304 on the RX family, and 32,768 on the M5 Pro. These speeds are observations under those conditions. They are not promises about every document length or runtime.

## Electricity is the smaller bill in these examples

Take a synthetic month with **10 million input tokens and 2 million generated tokens**, including any reasoning tokens. This is a way to estimate local compute time; it is not a claimed token allowance for a consumer subscription.

For each machine, divide input tokens by input-processing speed and generated tokens by output speed, then add the times. That produces about 28.9 active hours on the M4, 4.9 on the GPU, and 8.6 on the M5 Pro.

We don't have matched wall-power measurements for those exact runs. Instead, use explicit power assumptions: 40 watts of additional active draw for the existing M4, 300 watts for the upgrade, and 80 active watts for the dedicated machine. The first two add no idle time; the dedicated machine stays on for a 720-hour month and draws an assumed six watts while idle.

At $0.20 per kilowatt-hour, the result is:

| Scenario | Monthly electricity | Hardware spread over 36 months | Combined monthly cost |
|---|---:|---:|---:|
| Existing M4 | $0.23 | $0.00 | $0.23 |
| $820 upgrade | $0.29 | $22.78 | $23.07 |
| $2,300 dedicated computer | $0.99 | $63.89 | $64.88 |

Doubling the tariff to $0.40 per kilowatt-hour doubles the electricity column. It barely moves the combined totals for the two purchases. In these scenarios, hardware dominates the cost.

That conclusion depends on the workload. A machine working continuously, or a desktop left awake solely to serve a model, needs different active and idle hours. Setup, maintenance, repairs, financing, taxes, and the value of your time are excluded here. The hardware column assumes a 36-month life and no resale proceeds. The existing machine's purchase cost is treated as already incurred, which is appropriate for the incremental decision but does not mean the computer was free.

## How long until you get the money back?

Spreading a purchase over three years is useful for comparing monthly ownership costs. Cash payback asks a different question: when do avoided payments recover the money spent upfront?

Use this formula:

`payback months = new hardware cost ÷ (previous monthly AI spend − remaining monthly AI spend − local monthly running cost)`

If the denominator is zero or negative, there is no cash payback. Don't also subtract hardware amortization in this formula: the upfront purchase is already in the numerator.

Assume all the previous AI spending goes away and electricity is the only new recurring charge:

| Monthly spending eliminated | $820 upgrade | $2,300 dedicated computer |
|---|---:|---:|
| $20 | 41.6 months | 121.0 months |
| $100 | 8.2 months | 23.2 months |
| $200 | 4.1 months | 11.6 months |

The $20 row corresponds to a familiar price: [ChatGPT Plus lists $20 per month](https://help.openai.com/en/articles/6950777-what-is-chatgpt-plus). The other rows are spending scenarios. They are not claims that a local configuration matches any particular paid tier.

If the local model still leaves you paying $20 a month for the same subscription, you have eliminated none of that fee. If it lets you move from $100 of monthly spending to $20, the gross saving is $80. Put the actual difference into the formula.

An already-owned machine has no new purchase to recover. That makes it the least expensive way to find out whether a local model can handle enough of your work to cancel something.

## Can the model do the work?

There is benchmark evidence for capable small models, but it needs to be attached to the version that was tested.

At its August 2025 launch, OpenAI reported that [gpt-oss-20b delivered results similar to o3-mini on common benchmarks](https://openai.com/index/introducing-gpt-oss/), with a 16 GB memory target. That is a dated comparison with a named model. It doesn't establish parity with every model available through a subscription today.

For a more recent open-weight example, [Qwen's Qwen3.6-35B-A3B model card](https://huggingface.co/Qwen/Qwen3.6-35B-A3B) reports 85.2 on MMLU-Pro and 86.0 on GPQA Diamond. A [separate community run](https://llm-bench.io/benchmarks/cmu1q0opa003r01mu7dokzngt) fits a Q2_K version on an RX 7600 XT. The vendor's benchmark scores cannot simply be assigned to that much more compressed local version: the research did not establish its quality difference.

That leaves a practical requirement for a replacement claim: test the exact model and precision you intend to run on the work you currently pay for. A successful setup has to fit the needed context, finish at an acceptable speed, and produce answers you can use. A fast token rate alone doesn't establish those things.

## A subscription includes more than text generation

The [Plus feature list](https://help.openai.com/en/articles/6950777-what-is-chatgpt-plus) includes voice, image generation, file analysis, and Deep Research where available. Running one local text model does not supply that entire product. You may be able to assemble alternatives, but their software, services, and upkeep have to enter the comparison too.

This is where the cost calculation meets actual usage. Someone paying primarily for text drafting may have a different replacement threshold from someone relying on research, image tools, and a hosted coding workflow. The cost model doesn't need to guess: retain whatever hosted spending remains necessary.

With a $20 bill, either new-hardware scenario takes longer than our assumed three-year ownership period to pay back. With $100 genuinely eliminated each month, both pay back within it. A suitable machine already on your desk removes the largest expense from the experiment.

## Sources and calculation notes

Sources were checked on October 18, 2026. Speeds are reported measurements, hardware budgets and power draws are assumptions, and costs are our calculations. The time estimate assumes the published throughput applies to the monthly token mix; it omits model loading and scheduling overhead and gives no credit for local prefix caching. Tokens generated during reasoning belong in the output count.

The [calculation script](/research/ai-cost-calculations-2026-10.mjs) exposes the hardware budgets, electricity tariff, power, token volumes, lifetime, resale value, other running costs, and remaining hosted spend so the examples can be recomputed. It also keeps amortized cost separate from cash payback; resale proceeds affect ownership cost, but are not treated as cash received at purchase.