# Sunk Cost says a $3,499 local AI Mac needs 44 years to break even > Source: > Published: 2026-09-15 02:48:57+00:00 # Sunk Cost says a $3,499 local AI Mac needs 44 years to break even **The calculator turns local inference enthusiasm into a payback schedule, then shows how quickly cheaper APIs can make the hardware case worse.** By [RuntimeWire Staff](https://runtimewire.com/author/runtimewire-staff) ยท Published Primary source: [Sunk Cost](https://sunkcost.ai/) ## Why it matters Local AI hardware can deliver privacy, control and offline access, but cheap hosted inference makes token savings a weak justification for a new machine at moderate usage. The builder behind [Sunk Cost](https://sunkcost.ai/?ref=runtimewire) has put a price on one of local AI's favorite promises: buy the hardware once, stop paying model providers, and eventually come out ahead. For a representative coding workload on Apple's coming M5 Max [Mac Studio](https://www.apple.com/mac-studio/?ref=runtimewire), the calculator puts "eventually" [44 years away](https://sunkcost.ai/s/mac-studio-m5-max-64/qwen3.8-27b-q4/?u=500000&r=15&kwh=0.17&ctx=32768&cs=80&s=smartest&ref=runtimewire). The example pairs a $3,499 Mac Studio with 64 GB of unified memory and Alibaba's quantized [Qwen3.8 27B](https://runtimewire.com/models/qwen/qwen3.8-27b) model. At 500,000 tokens a day, with 15 input tokens for every output token, Sunk Cost estimates that local inference saves 22 cents daily against the hosted model price selected by the calculator. The machine remains $3,419 underwater after its 1st year and reaches break-even after processing about 7.97 billion tokens. That answer is less important than the product decision behind it. Sunk Cost's maker built the calculator around workload details that disappear from most local AI buying advice: prompt-to-output ratios, context size, generation speed, electricity, usable memory and falling API prices. The result gives prospective buyers a way to separate the appeal of owning compute from the narrower claim that ownership saves money. ### The Mac is fast. The API is cheap. Apple [introduced the M5 Max and M5 Ultra Mac Studio](https://www.apple.com/newsroom/2026/08/apple-introduces-new-mac-studio-with-m5-max-and-m5-ultra/?ref=runtimewire) on August 25th, pitching the desktop directly at on-device AI. Apple said the M5 Max configuration offers up to 614 GB/s of memory bandwidth, while the M5 Ultra can reach 512 GB of unified memory. Pre-orders opened with availability scheduled for September 22nd. RuntimeWire [covered Apple's local AI pitch](https://runtimewire.com/article/apple-mac-studio-m5-ultra-local-ai-price) when the hardware was announced. Sunk Cost supplies the missing buyer-side arithmetic. Fast local inference can still be poor financial substitution when a hosted provider sells the same model for a few dollars a month. For its [featured Mac Studio configuration](https://sunkcost.ai/hardware/mac-studio-m5-max-64/?ref=runtimewire), Sunk Cost assumes 468,750 input tokens and 31,250 output tokens per day. It prices that workload at roughly $6.94 a month through an OpenRouter endpoint, compared with 26 cents in monthly electricity. Hardware absorbs nearly the entire economic argument. The calculator estimates local generation at 24.7 tokens per second, rounded to 25. It derives that figure from the Mac's memory bandwidth, estimated bytes read per token and a 75% efficiency factor. Sunk Cost labels the number as an estimate. The M5 Max machine has yet to ship, leaving no measured production result for this configuration. The power figure carries a similar qualification. Sunk Cost uses 145 watts under load, borrowed from Apple's published maximum for the previous M4 Max Mac Studio because Apple has not supplied an equivalent figure for the 2026 model. That stand-in may overstate inference power consumption, which would make the local machine look slightly better once real measurements arrive. Electricity is already such a small portion of the calculation that the adjustment would barely dent the $3,499 upfront cost. ### Cloud prices can outrun the calculator The 44-year result depends on Sunk Cost's selected API rates of 32 cents per million input tokens and $2.50 per million output tokens, checked on September 3rd. Sunk Cost lets users model annual API price declines for that reason. Holding today's price flat forever flatters a machine purchased up front. Hosted inference vendors can spread new hardware, utilization gains and price competition across many customers; the owner of a Mac captures none of those improvements unless faster software or a better local model arrives. The calculator also charges local inference for waiting time. It estimates that a 1,000-token response takes 40 seconds on the Mac and 13 seconds through an API running at 80 tokens per second. Across the selected workload, Sunk Cost puts the added delay at about 15 minutes a day. That comparison is workload-dependent, though its inclusion improves the analysis: a cheaper answer has limited value when an operator repeatedly waits longer to receive it. ### Ownership still buys things the API cannot price Sunk Cost's conclusion stays deliberately narrow. The calculation excludes the value of keeping code and documents on-device, working without an internet connection, avoiding provider outages and using the Mac for unrelated work. Resale value also improves the ownership case. Prompt caching could reduce API costs further, while maintenance and downtime would weigh against local operation. Model quality creates another boundary. [Artificial Analysis scores Qwen3.8 27B](https://artificialanalysis.ai/models/qwen3-8-27b?ref=runtimewire) at 34 on its Intelligence Index under its highest reasoning setting. Sunk Cost describes that as broadly "Sonnet-class," while also warning that its capability bands are coarse judgments. The calculator is comparing local and hosted access to a similar open-weight model. It does not establish that a $3,499 Mac replaces the strongest proprietary systems available through cloud services. That restraint makes the tool useful. Sunk Cost's maker has avoided a universal verdict on local AI and built a scenario engine instead. A team with a steady, high-volume workload may find a short payback period. A developer buying primarily for privacy or experimentation may accept that the machine never pays for itself through token savings alone. For the moderate coding-assistant workload in the featured example, the hardware purchase behaves like an ownership choice rather than a cost optimization. Sunk Cost gives builders a clean way to admit that before checkout.