The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, is exceptionally well-suited for running the LLaVA 1.6 7B model. LLaVA 1.6 7B, requiring approximately 14GB of VRAM in FP16 precision, leaves a comfortable 10GB headroom on the RTX 3090. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory-related bottlenecks. The RTX 3090's 940 GB/s memory bandwidth further ensures efficient data transfer between the GPU and memory, crucial for maintaining high inference speeds. The presence of 10496 CUDA cores and 328 Tensor Cores also significantly accelerates the computations involved in running the LLaVA model, leading to improved performance compared to GPUs with fewer cores.
For optimal performance with LLaVA 1.6 7B on the RTX 3090, leverage the available VRAM by experimenting with batch sizes up to 7. Utilizing a framework like `vLLM` can further optimize throughput. While FP16 offers a good balance of speed and accuracy, consider experimenting with quantization techniques like Q4 or Q5 to potentially increase batch size or context length without sacrificing too much quality. Monitor GPU utilization and temperature to ensure the card operates within safe thermal limits, especially given its 350W TDP.