The primary limiting factor for running LLaVA 1.6 7B on an NVIDIA RTX 4060 is the VRAM. LLaVA 1.6 7B, when running in FP16 (half-precision floating point), requires approximately 14GB of VRAM to load the model and perform inference. The RTX 4060, however, only provides 8GB of VRAM. This 6GB deficit means that the model cannot be loaded entirely onto the GPU, leading to out-of-memory errors unless significant optimizations are applied. Memory bandwidth, while important for performance, becomes secondary when the model cannot even fit within the available VRAM. The RTX 4060's memory bandwidth of 0.27 TB/s would likely be a bottleneck if the model *could* fit, but it's not the immediate problem.
CUDA cores (3072) and Tensor Cores (96) are sufficient for accelerating the computations, but they cannot compensate for the lack of memory. The Ada Lovelace architecture offers good performance per watt, but the 115W TDP is irrelevant in this scenario because the model won't run without addressing the VRAM limitation. Without sufficient VRAM, the model would either fail to load, or it would rely heavily on system RAM via CPU offloading, resulting in drastically reduced performance. This would render real-time or interactive applications infeasible.
To run LLaVA 1.6 7B on an RTX 4060, you must significantly reduce the model's memory footprint. The most effective method is to use aggressive quantization techniques, such as Q4_K_M or even lower bit depths, offered by libraries like `llama.cpp` or using GPTQ quantization. This will compress the model, potentially bringing it within the 8GB VRAM limit. Be aware that quantization will reduce the model's accuracy to some degree.
Consider using `llama.cpp` with appropriate quantization settings and offloading as many layers as possible to the GPU. Experiment with different quantization levels to find a balance between VRAM usage and acceptable performance. If even with aggressive quantization the model doesn't fit, consider using a smaller vision model or a model with fewer parameters. Alternatively, consider using cloud-based inference services or upgrading to a GPU with more VRAM.