I am running Qwen3.6-35B-A3B locally on my 16GB mac with 7-9 tokens/second. Link - https://github.com/deepanwadhwa/samosa-chat

This is a GPT4 level model running locally with a decent speed on a 16gb ram macbook air.

If we just take into account output token generation for simplicity. With 5tps u get 18k tokens an hour. That would costs around 0.005USD from an inference provider.

I estimate that the server consumes probably around 500W during inference.

In Germany where 1kwh cost around 0.3USD, 18k tokens inferred locally would therefore cost 0.15USD which is 30x the costs of using an inference provider.

But for ppl who worry about their data, running locally might still be good. However, they should be aware, that it is much less efficient than using an inference provider.

The efficiency gap will also significantly increase as new GPUs will make inference much more efficient.

EDIT: I first thought it'd be 180k token, but thanks to someone mentioning in the comments, it is 18k. I guess with that, it will be tough unless u got electricity almost for free. Also, the inference providers are probably still using H200/H100 for those small models. Once they use GB300 or next year the new Ruby GPUs, inference will be cheaper by a factor of 30. By then, running local models will mostly be about privacy.

https://gist.github.com/hparadiz/f3596d00a62d8ebb2dadcc46ee5...

https://news.ycombinator.com/item?id=48354801

At national average electricity prices, that’s $1.35 per day. More during the summer if you have to cool the space.

If you run it 24/7 and ignore prompt processing time (not a good assumption at all) it would get around 400,000 tokens in a day.

That’s about $0.30 per million output tokens.

Coincidentally, that’s the same price for this model on OpenRouter right now, but OpenRouter token gen will be 8X faster.

There are a lot of good reasons to experiment with running LLMs locally, like if you don’t want any data leaving your house.

Don’t think that you’re going to come out ahead monetarily. I say this as someone with a lot more money invested in local inference hardware at home. It’s fun, but it’s not a way to save money.

FWIW I'm getting a hardware max of 20 tok/s (approx topping out the GPU's compute) on my custom local diffusiongemma port running on an M3.

A 10 year old Xeon is all you need

https://news.ycombinator.com/item?id=48353348

The fix is open upstream as PR #2138 (https://github.com/ikawrakow/ik_llama.cpp/pull/2138), awaiting review. Fair warning on the AI angle: the patch was written by Claude at my direction. The post is explicit about which parts were me and which weren't. Happy to answer questions about either the bug or the workflow.

Android studio connected to a local model disconnects automatically after 10 minutes. How set this limit to 12 hours or remove it completely?

I could run my LM studio model all night... but I cant, since Android studio times out after a hard limit of 10M.

This is not related to number of tokens.

I tried Googling, searching for settings in Android studio, even created a stackoverflow post - but zero information. Jetbrains mentions "remote agent timeout mechanism" - but after changing it, nothing happens.