The NVIDIA RTX 4060 Ti 8GB is an excellent choice for running the CLIP ViT-L/14 model. With 8GB of GDDR6 VRAM, it comfortably exceeds the model's 1.5GB VRAM requirement, leaving a substantial 6.5GB headroom for larger batch sizes or other concurrent tasks. The Ada Lovelace architecture provides a significant performance boost for AI workloads, further enhanced by its 4352 CUDA cores and 136 Tensor cores which are specifically designed to accelerate matrix multiplications, a core operation in deep learning models like CLIP. The memory bandwidth of 0.29 TB/s, while not the highest available, is sufficient for efficiently transferring data between the GPU and its memory for this particular model.
For optimal performance with the CLIP ViT-L/14 model on the RTX 4060 Ti, start with a batch size of 32, as this should fully utilize the available resources without exceeding the VRAM capacity. Experiment with different inference frameworks like PyTorch or TensorFlow, leveraging TensorRT for potential speedups. Monitor GPU utilization and memory usage to fine-tune batch size and identify any potential bottlenecks. Consider optimizing your code to minimize data transfer between CPU and GPU to further improve inference speed. If you encounter VRAM issues with larger batch sizes, explore gradient checkpointing to reduce memory footprint.