The primary limiting factor for running LLaVA 1.6 13B on an NVIDIA RTX 4070 is the GPU's VRAM capacity. LLaVA 1.6 13B, when using FP16 (half-precision floating point), requires approximately 26GB of VRAM to load the model and necessary buffers. The RTX 4070, equipped with 12GB of GDDR6X VRAM, falls significantly short of this requirement, resulting in a VRAM headroom deficit of 14GB. While the RTX 4070's Ada Lovelace architecture and 5888 CUDA cores offer substantial computational power, the inability to load the entire model into VRAM prevents effective execution.
Furthermore, even if techniques like offloading layers to system RAM were employed, the performance would be severely degraded. Accessing data from system RAM is substantially slower than accessing it from VRAM, leading to a significant bottleneck. The RTX 4070's memory bandwidth of 0.5 TB/s is excellent for its class, but this bandwidth is only fully utilized when data resides within the GPU's memory. The combination of insufficient VRAM and slower data transfer from system RAM makes running LLaVA 1.6 13B in its full FP16 precision impractical on the RTX 4070.
Given the VRAM limitations, running LLaVA 1.6 13B in its full FP16 precision on an RTX 4070 is not feasible. To make it work, consider aggressive quantization techniques like Q4_K_S or even lower precisions using llama.cpp or similar frameworks. This reduces the model's memory footprint, potentially bringing it within the 12GB VRAM limit. However, expect some loss in accuracy compared to the FP16 version.
Alternatively, explore using a smaller model variant of LLaVA or a different multimodal model altogether that requires less VRAM. Another option would be to offload some layers to system RAM, but this will drastically reduce inference speed. If high performance is crucial, consider upgrading to a GPU with more VRAM, such as an RTX 3090, RTX 4080, or an AMD Radeon RX 7900 XTX.