Most of the open source stuff people are talking about is things like running a quantized 33B parameter LLaMA model on a 3090. That can be done on consumer hardware, but isn't quite as good at general purpose queries as GPT-4. Depending on your use case and your ability to fine tune it, that might be sufficient for a number of applications. Partcularly if you've got a very specific task.
However, if you're willing to spend, there are bigger models available (e.g. Falcon 40B, LLaMA 65B) that can be run on data server class machines, if you're willing to spend $15-20K.
Will that get you GPT-4 level inference? Probably not (though it is difficult to quantify); will it get you a high-quality model that can be further fine-tuned on your own data? Yes.
For the smaller models, the fine-tunes for various tasks can be fairly effective; in a few more weeks I expect that they'll have continued to improve significantly. There's new capabilities being added every week.
The biggest weakness that's been highlighted in research is that the open source models aren't as good at the wide range of tasks that OpenAI's RLHF has covered; that's partly a data issue and partly a training issue.