The NVIDIA RTX 4080, with its 16GB of GDDR6X VRAM, 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 and activations. The RTX 4080 provides a comfortable 2GB VRAM headroom, which is beneficial for handling larger batch sizes or accommodating other processes running on the GPU. This headroom helps prevent out-of-memory errors and ensures stable operation during inference. The RTX 4080's memory bandwidth of 0.72 TB/s is also crucial for efficiently transferring data between the GPU and memory, contributing to faster inference speeds.
For optimal performance, consider using a framework like `vLLM` or `text-generation-inference` which are designed for fast inference. While FP16 works, explore quantization techniques like Q4 or Q5 to potentially reduce VRAM usage further and increase throughput, though this may come with a slight reduction in accuracy. Experiment with batch sizes, starting with 1, and gradually increasing it until you observe performance degradation or encounter memory limitations. Monitor GPU utilization to ensure that the GPU is being fully utilized and adjust settings accordingly.