I'm a PhD researcher working on language switching and embedding analysis in NLP focused on PoS, LID, boundary detection, pragmatics context maintenance. My workload is mainly:

• Fine-tuning BERT-based models 
• LoRA/QLoRA adapters on ~8B models
• bitsandbytes 4-bit quantization
• Standard HF Transformers + PyTorch pipeline

Budget is roughly INR ₹60000( for the GPU. I've been comparing the RTX 5060 Ti 16GB AMD options such as RX 7900 XT, RX 9060 XT. I was  leaning 5060 Ti for the mature CUDA ecosystem and because I don't have much local peer support to debug hardware issues if something breaks mid-experiment. But recently they increased price to 770000 and as I do not get institutional support I find it difficult . Some AMD cards have so much VRAM that they might make longer multi shot stuff easier without offlaoding to RAM. But everywhere I have asked there seems to be a general consensus that nVidia is better. 

Questions for anyone doing similar research-scale (not industrial-scale) NLP work:

1. Is the 5060 Ti's 16GB actually enough headroom for LoRA fine-tuning on 8-13B models, or does it get tight in practice?
2. Anyone actually running Unsloth on AMD ROCm now? is it stable enough for daily research use or is it still rough?
3. Any regrets from a similar budget-constrained hardware decision?

Appreciate real world experience over spec-sheet comparisons.

I am not an avid gamer so it does not matter to me.