The NVIDIA RTX 4060 Ti 16GB, while a capable mid-range GPU based on the Ada Lovelace architecture, falls short of the VRAM requirements for running LLaVA 1.6 13B in FP16 (16-bit floating point) precision. LLaVA 1.6 13B, a large vision model, demands approximately 26GB of VRAM when using FP16. The RTX 4060 Ti 16GB only provides 16GB, resulting in a deficit of 10GB. This VRAM shortfall will prevent the model from loading and running effectively, leading to out-of-memory errors. The RTX 4060 Ti's memory bandwidth of 0.29 TB/s, while decent for its class, would further constrain performance even if the VRAM requirements were met. The limited memory bandwidth will bottleneck the data transfer rates between the GPU and VRAM, impacting inference speed.
Due to the significant VRAM deficit, running LLaVA 1.6 13B in FP16 on the RTX 4060 Ti 16GB is not feasible without substantial optimization. Consider using aggressive quantization techniques, such as Q4_K_M or even lower, to reduce the model's memory footprint. This will significantly degrade the model's accuracy. Alternatively, explore offloading layers to system RAM, but expect a drastic performance decrease. If high performance is crucial, consider upgrading to a GPU with at least 24GB of VRAM, such as an RTX 3090, RTX 4080, or an NVIDIA A40.