The NVIDIA RTX 4070 Ti SUPER, while a powerful card based on the Ada Lovelace architecture, presents a challenge when running the LLaVA 1.6 13B model due to its 16GB of GDDR6X VRAM. LLaVA 1.6 13B, especially in FP16 precision, demands approximately 26GB of VRAM to load the model and its associated overhead. This creates a significant VRAM deficit of 10GB, preventing the model from running in its native FP16 format. The 4070 Ti SUPER's memory bandwidth of 0.67 TB/s is adequate for smaller models, but becomes a bottleneck when attempting to offload layers to system RAM due to the VRAM shortage. Without sufficient VRAM, the model cannot be loaded completely onto the GPU, leading to errors or extremely slow performance due to constant data swapping between system RAM and GPU memory. The 8448 CUDA cores and 264 Tensor cores are underutilized in this scenario, as the primary limitation is memory capacity, not compute capability.
To run LLaVA 1.6 13B on the RTX 4070 Ti SUPER, you'll need to employ aggressive quantization techniques. Quantization reduces the memory footprint of the model by representing weights and activations with fewer bits. Consider using a quantization level of Q4 or even Q3. This will significantly reduce the VRAM requirement, potentially bringing it within the 16GB limit. Furthermore, explore inference frameworks that support CPU offloading, such as llama.cpp, which allows you to offload some layers to system RAM. Be aware that offloading will significantly impact inference speed. As a last resort, consider using a smaller model, such as a 7B variant of LLaVA, which would be more manageable with the available VRAM.