All of it is really about scale, same reason as a single person you can't sell french fries as cheap as McDonalds.

Serving LLM inference is more efficient with more concurrent requests. For a single inference request, the bottleneck is GPU memory bandwidth, as the inference engine spends most of its time waiting to move values in memory. With concurrent requests, it's possible to service multiple requests in a single pass through the model weights, meaning you can utilize more of the GPU/APU's compute.

So you have to price your local hardware against however many subscriptions you would need to run a dozen concurrent agents around the clock.

But even then, I would guess subscriptions are priced on the basis that they don't expect every user to utilize their entire usage limit. I.e. power users are subsidized by casual users.

Because the datacenter setups serve multiple users concurrently while your mac mini only serves one. You would also lose money hand over fist renting/buying 4xH200s just to serve deepseek flash to yourself.

These datacenters run the GPU's close to 100% usage 24/7. The Mac mini at home probably has <25% utilization. And then there's economies of scale, wholesale discounts, etc.

The Mac mini GPU also isn't as optimized as the datacenter GPUs

You can batch calls if you're serving a lot of people at once.