This is the AI I am excited for. Data and execution local to my machine. I think Intel is betting on this with the copilot included processors. I hope ollama or other local AI services will be able to utilize these co-processors soon.
The bigger problem is that this NPU hardware isn't built around scaling to larger models. It's laser-focused on dense computation and low-precision inference, which usually isn't much more efficient than running the same matmul as a compute shader. For Whisper-scale models that don't require insanely high precision or super sparse decoding, NPU hardware can work great. For LLMs it is almost always going to be slower than a well-tuned GPU.
Of those who do, I can see students and researchers benefiting from small models. Students in particular are famously short on money for fancy hardware.
My experience trying one of the Phi models (I think 3, might have been 2) was brief, because it failed so hard: my first test was to ask for a single page web app Tetris clone, and not only was the first half the output simply doing that task wrong, the second half was a sudden sharp turn into python code to train an ML model — it didn't even delimit the transition, one line javascript, the next python.
The Phi models are tiny LMs, maybe SLM is more fitting label than LLM (Large -> Small). As such, you cannot throw even semi-complicated problems at them. Things like "autocomplete" and other simpler things are the use cases you'd use it for, not "code this game for me", you'll need something much more powerful for that.
Indeed, clearly.
However, it was tuned for chat, and people kept telling me it was competitive with the OpenAI models for coding.
Think of it like an extended auto complete.
I believe Microsoft calls them "SLMs - Small Language Models".
And that is hosted in a jurisdiction that forces them to take it seriously, e.g. Mistral in France that has to comply with GDPR and any AI and privacy regulations out of the EU.