The NVIDIA RTX A5000, with its 24GB of GDDR6 VRAM, falls significantly short of the 68GB required to load and run LLaVA 1.6 34B in FP16 precision. This memory shortfall means the entire model cannot reside on the GPU simultaneously, leading to out-of-memory errors or extremely slow performance due to constant data swapping between the GPU and system RAM. While the A5000's 770 GB/s memory bandwidth is respectable, it cannot compensate for the sheer lack of VRAM. The 8192 CUDA cores and 256 Tensor cores would be beneficial if the model fit in memory, but they are rendered largely ineffective in this scenario due to the VRAM bottleneck. The Ampere architecture is capable, but memory capacity is the limiting factor here.
Due to the substantial VRAM deficit, running LLaVA 1.6 34B on the RTX A5000 without significant modifications is not feasible. Consider using quantization techniques like Q4 or even lower precisions to drastically reduce the model's memory footprint. Alternatively, explore using CPU offloading, although this will severely impact inference speed. A more practical approach might involve using a smaller model, such as LLaVA 1.5 7B or exploring distributed inference across multiple GPUs if high performance is a necessity. Another option is to use cloud-based GPU instances that offer the required VRAM, such as those offered by NelsaHost.