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Apple Silicon costs more than OpenRouter

williamangel.net

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Re: Apple Silicon costs more than OpenRouter

#3
Will this cost structure always be this way and are there other benefits to not running your LLM on the cloud?

E.g.

Privacy

Uptime

Future cost structure controls

This is a field that has moved very quickly. And it has moved in a direction to try to trap users into certain habits. But these habits might not best align with what best benefits end users today or some time in the future.

Re: Apple Silicon costs more than OpenRouter

#6
Unless I'm misunderstanding, this is counting the entire laptop in the cost of generating tokens. The calculation seems to omit that, in addition to receiving LLM output, you have also received a laptop in exchange for your money. If you intend to put this machine in a dark corner and run it solely as a token-munching server, a laptop would be an exceptionally poor choice of technology for this purpose. But if you intend to use the laptop as a laptop, having a laptop is a pretty big benefit over not having a laptop.

You also get the benefit of privacy, freedom from censorship, and control over the model used (i.e. it will not be rugpulled on you in three months after you've built a workflow around a specific model's idiosyncrasies).

Re: Apple Silicon costs more than OpenRouter

#9
The author only compared output token costs -- but for typical agentic workloads, input tokens dominate the costs by a large margin. Running inference locally, input tokens are, to first order, free. (They only generate implicit costs through higher time-to-first-token, higher power use, and lower token output speed).

Re: Apple Silicon costs more than OpenRouter

#10
I don't hear people debating which is cheaper, local or cloud run models. The conversation, at least what I hear, is a lot of the time users are not utilizing an awful lot of tickets all the time, those providers will be paid if you never use them. If 80% - 90% of the work I and my team are doing with Ai is grunt work, write tests for this, implement a FFT here, write the dB query for X. Nothing exhausting. Those who are using AI for whole cloth "vibe coded" applications and services are definitely better suited to cloud. If a work laptop can run my local models and get my works needed performance for development, why wouldn't I as a company prefer that?

Add to that the privacy improvements and data protection and potentially further specific inferance if needed it's a no brainer.

Again, Ai is a tool, and the right tool for the job, I would wager with no evidence looked up, is that the majority of Devs would be happy with 10-30 per second locally.

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