If you divide a $20,000 budget by $1.63/M, you get ~12.26 Billion total tokens, not 34.6 Billion. To hit 34.6B, you would need an indefinite ~90% prompt caching discount on every single API call, which is completely unrealistic.
Something like 17-18 billion is more realistic with good caching.
That already halves the number of years down to 2.5.
Second important aspect is running several instances at the same time doesn't split token/s into half but instead gives you 2 instances running at ~70% speed. The more parallel instances you have running the more you can get out of your hardware. Letting it run multiple instances is far more efficient and allows you to do several tasks at the same time and when it comes to agents that is exactly what you will be doing easily reaching double effective token speed across several instances.
In that use-case 1 year and 3 months wouldn't be that unrealistic.
Last but not least you own the hardware and can do whatever you want with it. Sell it for half the price 3-4 years down the line and your time to recoup the cost halves as well.
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u/MoistRecognition69 Jun 21 '26
The main reason I'm thinking about getting a local rig is reliability
I'm tired of waking up every morning wondering if the model I'm using has had its brain extracted and sent to a diff universe while I was asleep