I believe that this is doable - my pipeline is generally closer to 400ms without RAG and with Mixtral, with a lot of non-ML hacks to get there. It would also definitely be doable with a joint speech-language model that removes the transcription step.
For these use cases, time to first byte is the most important metric, not total throughput.
The most interesting applications of LLMs are not chatbots.
What are they then? Every use case I’ve seen is either a chatbot or like a copy editor which is just a long form chatbot.
Think about the implications of that. I bet you can come up with some pretty cool use cases that don't involve you talking to something over chat.
One example:
I think we'll be seeing a lot of "general detectors" soon. Without training or predefined categories, get pinged when (whatever you specify) happens. Whether it's a security camera, web search, event data, etc
In your opinion, what are the most interesting?