Earnings Calculator

Provider Earnings Calculator

Estimate how much your Apple Silicon Mac can earn serving inference on the Darkbloom network.

Turn your Mac into a provider

Set up your Apple Silicon Mac to serve inference and earn from the Darkbloom network.

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Select Your Hardware

1. Mac Type

2. Chip

3. Memory

Not sure about your specs? Click > About This Mac to check.

MacBook ProM4 Max48 GB546 GB/s20W idle → 50W infer

Model

Auto-selected: most profitable for your hardware

Paused for maintenance — image routing is disabled right now. Earnings projection shown for reference; no image requests are being served currently.
12hours of active inference per day
1 hr12 hrs24 hrs
$/kWh

US avg: $0.15 | EU avg: $0.25 | CA avg: $0.22

Estimated Earnings

Serving FLUX.2 Klein 4B at 12 hrs/day

Monthly net earnings

$1,325

$15,902 / year

Throughput

2,457 images/hr

Monthly revenue

$1326.78

Monthly electricity

-$1.62

Electricity % of revenue

0.1%

Revenue per hour

$3.6855

Electricity per hour

$0.0045

Net per hour

$3.6810

Provider sharePayouts are currently processed manually. Automatic payouts coming soon.

100%

How this is calculated

scaled_images/hr = 450 * (546 GB/s / 100 GB/s) = 2457

revenue/hr = 2457 images/hr * $0.0015/image = $3.6855

marginal_watts = 50W (inference) - 20W (idle) = 30W

elec/hr = (30W / 1000) * $0.15/kWh = $0.0045

net/hr = $3.6855 - $0.0045 = $3.6810

monthly = $3.6810 * 12 hrs/day * 30 days = $1325.16

Your Mac earns more idle than...

662 Spotify Premium subscriptions
265 lattes per month
88 Netflix Standard plans
a 26-day parking meter
18x your home internet bill
$15,902/yr — a nice side income

These are estimates only. We do not guarantee any specific utilization or earnings. Actual earnings depend on network demand, model popularity, your provider reputation score, and how many other providers are serving the same model.

When your Mac is idle (no inference requests), it consumes minimal power — you don't lose significant money waiting for requests. The electricity costs shown only apply during active inference.

Text models typically see the highest and most consistent demand. Image generation and transcription requests are bursty — high volume during peaks, quiet otherwise.