The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, is well-suited for running the LLaVA 1.6 7B vision model. LLaVA 1.6 7B in FP16 precision requires approximately 14GB of VRAM for the model weights, leaving a substantial 10GB headroom for processing images, intermediate activations, and batch processing. The RTX 3090 Ti's memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and memory, which is critical for maintaining high inference speeds. Furthermore, the 10752 CUDA cores and 336 Tensor Cores significantly accelerate the matrix multiplications and other computations inherent in deep learning models like LLaVA 1.6 7B.
For optimal performance with LLaVA 1.6 7B on the RTX 3090 Ti, start with a batch size of 7 and a context length of 4096 tokens. Experiment with quantization techniques such as Q4_K_M or Q5_K_M using llama.cpp to potentially increase throughput without significantly impacting accuracy. Monitor GPU utilization and memory usage to fine-tune these parameters. If you encounter VRAM limitations with larger batch sizes or longer context lengths, consider reducing the batch size or exploring techniques like gradient checkpointing (if supported by the inference framework) to reduce memory footprint.