A properly finetuned model can perform better for a use case but even with PEFT/LoRAs, finetuning and managing "smaller" open-source LLMs (7B params) models like Llama3 is annoying. That's partially why the even-smaller ~2B Phi series of models took off.
A 0.5B model may not be that great out of the box but there's a lot of oppertunity if it's responsive to finetuning.
Ye smaller models are fantastic for finetuning and probably on device applications - they can act as like a first pass on most LLM applications, then if it requires a larger model to intervene, then it can pass if off to some larger one. I do have a Colab to finetune 0.5b 2x faster here for those interested: https://colab.research.google.com/drive/1-7tjDdMAyeCueyLAwv6...
A 0.5B model may not be that great out of the box but there's a lot of oppertunity if it's responsive to finetuning.