The NVIDIA RTX 4070 Ti, with its 12GB of GDDR6X VRAM and Ada Lovelace architecture, is exceptionally well-suited for running the CLIP ViT-L/14 vision model. CLIP ViT-L/14 requires approximately 1.5GB of VRAM when using FP16 precision, leaving a substantial 10.5GB of headroom on the 4070 Ti. This ample VRAM allows for large batch sizes and concurrent processing of multiple images or requests, significantly boosting throughput. The 4070 Ti's memory bandwidth of 0.5 TB/s further ensures that data can be efficiently transferred between the GPU and memory, preventing bottlenecks during inference.
Given the comfortable VRAM headroom, users should prioritize maximizing batch size to improve throughput. Experiment with batch sizes up to 32, observing performance and adjusting as needed. Utilizing TensorRT or ONNX Runtime can further optimize inference speed by leveraging the 4070 Ti's Tensor Cores. Consider using mixed precision (FP16) to balance accuracy and speed. If encountering any memory limitations with larger batch sizes, reduce the batch size incrementally. Additionally, ensure that the latest NVIDIA drivers are installed to benefit from performance improvements and bug fixes specific to the Ada Lovelace architecture.