The NVIDIA RTX 4080, with its 16GB of GDDR6X VRAM and Ada Lovelace architecture, is exceptionally well-suited for running the CLIP ViT-L/14 model. The model's relatively small size of 0.4 billion parameters and a modest 1.5GB VRAM footprint in FP16 precision leaves a substantial 14.5GB VRAM headroom. This ample VRAM capacity ensures that the RTX 4080 can easily accommodate the model alongside larger batch sizes and other processes without encountering memory limitations. The RTX 4080's 720 GB/s memory bandwidth and 9728 CUDA cores further contribute to its ability to efficiently handle the computational demands of CLIP ViT-L/14.
Given the RTX 4080's robust capabilities, users can explore larger batch sizes (up to 32) to maximize throughput. Experiment with different inference frameworks like TensorRT or ONNX Runtime to potentially optimize performance further. While FP16 provides a good balance between speed and accuracy, consider using FP32 if higher precision is needed, although this will increase VRAM usage and may reduce batch size. For maximum performance, ensure the latest NVIDIA drivers are installed and the GPU is properly cooled to prevent thermal throttling.