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My hope was to find a system which does ASR, then LLM processing with MCP use and finally TTS: "Put X on my todo list" / "Mark X as done" -> LLM thinks, reads the todo list, edits the todo list, and tells me "I added X to your todo list", ... "Turn all the lights off" -> llm thinks and uses MCP to turn off the lights -> "Lights have been turned off". "Send me an email at 8pm reminding me to do" .... "Email has been scheduled for 8pm"

That's all I want. It does not have to be fast, but it must be capable of doing all of that.

Oh, and it should be energy efficient. Very important for a 24/7 machine.



You can already do that on most desktop GPU's (even going as far as prev gen Nv 1050/1060/1070 for example).

You'll need a model able to work with tools, like llama 3.2 (https://huggingface.co/meta-llama), serve it, hook up MCPs, include a STT interface, and you're cooking.


Even a bottom of the barrel N95 has audio acceleration features helping with speech to text, but the LLM inference part still will be far from being efficient.

Plus, you need to keep the card at "ready" state, you can't idle/standby it completely.


Energy efficient LLM inference (for now) is as realistic as existence of perpetual motion.


I wouldn't mind if it burns 200 watts while it does the task, as long as it idles at below 30W


NVIDIA H200 idles at 75 watts. I'm not keeping my hopes high on that, either.


To be fair, if I paid $30k+ for an H200, I’d want it to be making money 24/7 rather than idling, so the idle power draw would be strictly theoretical.


It's not always about money.


You probably can do this now. Non-generative LLMs don't need to be as big so something like Gemma 4B on the CPU will work.

You may have better results with semi-templated responses though.




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