The NVIDIA RTX 4070 Ti SUPER, while a capable card with 16GB of GDDR6X VRAM, falls significantly short of the VRAM requirements for running LLaVA 1.6 34B in FP16 precision. LLaVA 1.6 34B, a large vision model, necessitates approximately 68GB of VRAM to load the model and its associated data for inference. The 52GB VRAM deficit means the model cannot be loaded onto the RTX 4070 Ti SUPER without employing substantial memory-saving techniques. Furthermore, even with optimizations, the limited memory bandwidth of 0.67 TB/s could become a bottleneck, especially with larger batch sizes or context lengths, impacting the overall inference speed. The Ada Lovelace architecture's Tensor Cores will assist with accelerating the matrix multiplications inherent in transformer models, but this advantage is overshadowed by the VRAM constraint.
Due to the significant VRAM difference, directly running LLaVA 1.6 34B on the RTX 4070 Ti SUPER is impractical without aggressive quantization. Consider using 4-bit quantization (e.g., Q4_K_M or similar) via llama.cpp or a similar framework to drastically reduce the model's memory footprint. Even with quantization, experiment with smaller context lengths and batch sizes to avoid out-of-memory errors. If performance remains unsatisfactory, consider using cloud-based inference services that provide access to GPUs with sufficient VRAM, or explore smaller models that fit within the 16GB VRAM limit of the RTX 4070 Ti SUPER. Distributed inference across multiple GPUs is another option, but it requires significant setup and expertise.