The NVIDIA RTX 4060 Ti 8GB is not directly compatible with the LLaVA 1.6 34B model due to insufficient VRAM. LLaVA 1.6 34B, when run in FP16 (half-precision floating point), requires approximately 68GB of VRAM to load the model and perform computations. The RTX 4060 Ti 8GB only provides 8GB of VRAM, resulting in a VRAM deficit of 60GB. This means the model cannot be loaded onto the GPU in its full FP16 form.
Furthermore, even if aggressive quantization techniques are employed to reduce the model's memory footprint, the relatively limited memory bandwidth of 0.29 TB/s on the RTX 4060 Ti 8GB could become a bottleneck, significantly impacting inference speed. The 4352 CUDA cores and 136 Tensor cores will remain largely underutilized due to the VRAM constraint. Without sufficient memory, the model cannot effectively leverage the parallel processing capabilities of the GPU, leading to extremely slow or non-functional performance.
Due to the significant VRAM limitation, running LLaVA 1.6 34B directly on the RTX 4060 Ti 8GB is impractical without substantial compromises. Consider using cloud-based inference services like NelsaHost which can provide access to GPUs with sufficient VRAM. Alternatively, explore extreme quantization methods such as 4-bit quantization using llama.cpp or similar frameworks. Even with quantization, performance will likely be slow. For local use, a smaller model variant or a GPU with significantly more VRAM (24GB or more) is highly recommended. Another option is offloading some layers to system RAM, but this will drastically reduce inference speed.