The AMD Instinct MI300X is a remarkable piece of hardware: 192 GB of HBM3 memory in a single device. For LLM fine-tuning, VRAM is often the binding constraint — it dictates batch size, sequence length, and whether you need to quantize at all. With 192 GB available, we trained Qwen3-1.7B with LoRA in full fp16 without any 4-bit or 8-bit quantization hacks.

More importantly, the goal was to prove that the HuggingFace ecosystem — Transformers, PEFT, TRL, Accelerate — works seamlessly on ROCm. It does. The same training code that runs on CUDA runs on ROCm with three environment variables set:

MedMCQA is a large-scale multiple-choice question dataset derived from Indian medical entrance exams (AIIMS, USMLE-style). Each example contains:

For this project we used 2,000 training samples — a deliberately small slice to demonstrate that meaningful fine-tuning is achievable quickly. Training took approximately 5 minutes on the MI300X.