The NVIDIA RTX A6000, with its 48GB of GDDR6 VRAM, falls short of the 68GB required to load the LLaVA 1.6 34B model in FP16 (half-precision floating point) format. This discrepancy means the model cannot be directly loaded onto the GPU for inference. The A6000's 770 GB/s memory bandwidth is substantial and would be beneficial *if* the model fit in VRAM, allowing for rapid data transfer between the GPU and its memory. The Ampere architecture's 10752 CUDA cores and 336 Tensor Cores would provide significant computational power, but they cannot be effectively utilized if the model exceeds the available VRAM.
Without sufficient VRAM, the system would likely resort to offloading parts of the model to system RAM, which is significantly slower than GPU memory. This would drastically reduce inference speed, making real-time or interactive applications impractical. Even with optimizations like quantization, the base VRAM requirement is too high for the A6000 to handle the LLaVA 1.6 34B model in its entirety. The model's context length of 4096 tokens further exacerbates the VRAM demand, as larger context windows require more memory to store intermediate calculations during inference.
Due to the VRAM limitation, running LLaVA 1.6 34B on a single RTX A6000 is not feasible without significant compromises. Consider using quantization techniques like Q4 or even lower to reduce the model's memory footprint. However, even with aggressive quantization, performance might be significantly degraded. Alternatively, explore distributed inference solutions across multiple GPUs, if available, or consider using cloud-based GPU instances with larger VRAM capacities, such as A100 or H100 GPUs. Another option is to use a smaller model, such as LLaVA 1.5 7B or 13B.