The primary limiting factor in running large language models (LLMs) like LLaVA 1.6 13B is the available VRAM on the GPU. LLaVA 1.6 13B, when using FP16 (half-precision floating point) for weights, requires approximately 26GB of VRAM to load the model and perform inference. The NVIDIA RTX 4080, while a powerful GPU, is equipped with 16GB of GDDR6X VRAM. This results in a VRAM deficit of 10GB, meaning the model cannot be loaded entirely onto the GPU for processing. Attempting to run the model without sufficient VRAM will lead to errors, significantly reduced performance due to offloading to system RAM (which is much slower), or outright failure.
Given the VRAM limitations, running LLaVA 1.6 13B directly on the RTX 4080 in FP16 is not feasible. To make it work, you'll need to employ aggressive quantization techniques, such as Q4 or even lower bit precisions. Consider using llama.cpp or similar frameworks that excel at quantized inference. Alternatively, explore cloud-based solutions or GPUs with higher VRAM capacity if the highest possible performance is crucial. Distributed inference across multiple GPUs is another advanced option, but it adds significant complexity.