

For inference, the bnb_4bit_quant_type does not have a huge impact on performance. However, to remain consistent with the model weights, you should use the bnb_4bit_compute_dtype and torch_dtype values.
Nested quantization
Nested quantization is a technique that can save additional memory at no additional performance cost. This feature performs a second quantization of the already quantized weights to save an addition 0.4 bits/parameter. For example, with nested quantization, you can finetune a Llama-13b model on a 16GB NVIDIA T4 GPU with a sequence length of 1024, a batch size of 1, and enabling gradient accumulation with 4 steps.