I think most of the interesting applications for these small models are in the form of developer-driven automations, not chat interfaces.

A common example that keeps popping up is a voice recorder app that can provide not just a transcription of the recording (which you don't need an LLM for), but also a summary of the transcription, including key topics, key findings, and action items that were discussed in a meeting. With speaker diarization (assigning portions of the transcript to different speakers automatically), it's even possible to use an LLM to assign names to each of the speakers in the transcript, if they ever identified themselves in the meeting, and then the LLM could take that and also know who is supposed to be handling each action item, if that was discussed in the meeting. That's just scratching the surface of what should be possible using small LLMs (or SLMs, as Microsoft likes to call them).

An on-device LLM could summarize notifications if you have a lot of catching up to do, or it could create a title for a note automatically once you finish typing the note, or it could be used to automatically suggest tags/categories for notes. That LLM could be used to provide "completions", like if the user is writing a list of things in a note, the user could click a button to have that LLM generate several more items following the same theme. That LLM can be used to suggest contextually-relevant quick replies for conversations. In a tightly-integrated system, you could imagine receiving a work phone call, and that LLM could automatically summarize your recent interactions with that person (across sms, email, calendar, and slack/teams) for you on the call screen, which could remind you why they're calling you.

LLMs can also be used for data extraction, where they can be given unstructured text, and fill in a data structure with the desired values. As an example, one could imagine browsing a job posting... the browser could use an LLM to detect that the primary purpose of this webpage is a job posting, and then it could pass the text of the page through the LLM and ask the LLM to fill in common values like the job title, company name, salary range, and job requirements, and then the browser could offer a condensed interface with this information, as well as the option to save this information (along with the URL to the job posting) to your "job search" board with one click.

Now, it might be a little much to ask a browser to have special cases for just job postings, when there are so many similar things a user might want to save for later, so you could even let the user define new "boards" where they describe to a (hopefully larger) LLM the purpose of the board and the kinds of information you're looking for, and it would generate the search parameters and data extraction tasks that a smaller LLM would then do in the background as you browse, letting the browser present that information when it is available so that you can choose whether to save it to your board. The larger LLM could still potentially be on-device, but a more powerful LLM that occupies most of the RAM and processing on your device is something you'd only want to use for a foreground task, not eating up resources in the background.

LLMs are interesting because they make it possible to do things that traditional programming could not do in any practical sense. If something can be done without an LLM, then absolutely... do that. LLMs are very computationally intensive, and their accuracy is more like a human than a computer. There are plenty of drawbacks to LLMs, if you have another valid option.