The NVIDIA RTX 4060 Ti 16GB is an excellent choice for running the CLIP ViT-L/14 model. With 16GB of GDDR6 VRAM, it comfortably exceeds the model's 1.5GB requirement, leaving a significant 14.5GB headroom for larger batch sizes, higher resolutions, or concurrent tasks. The Ada Lovelace architecture provides a substantial number of CUDA cores (4352) and Tensor Cores (136), which are crucial for accelerating the matrix multiplications and other computations inherent in vision 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 VRAM, ensuring smooth operation.
The CLIP ViT-L/14 model, with its 0.4 billion parameters, is relatively small compared to larger language models, making it a good fit for mid-range GPUs like the RTX 4060 Ti. The model's context length of 77 tokens is also manageable, allowing for quick processing of image and text inputs. The abundance of VRAM allows the user to experiment with larger batch sizes to increase throughput, potentially at the cost of increased latency. The Tensor Cores on the RTX 4060 Ti will be leveraged to accelerate FP16 operations, leading to faster inference times compared to running on CPUs or GPUs without dedicated Tensor Cores.
For optimal performance, utilize a framework like PyTorch or TensorFlow with CUDA support to take full advantage of the RTX 4060 Ti's capabilities. Experiment with different batch sizes to find the sweet spot between throughput and latency. Start with a batch size of 32 and adjust as needed. Monitor GPU utilization and VRAM usage to identify potential bottlenecks. Consider using mixed precision training (FP16) if you are fine-tuning the model to further accelerate training and reduce VRAM consumption. This setup provides a solid foundation for both inference and fine-tuning of CLIP ViT-L/14.
While the 16GB VRAM provides ample headroom, consider optimizing your image preprocessing pipeline to minimize memory usage. Resizing images to smaller dimensions before feeding them into the model can significantly reduce VRAM consumption, especially when working with large batches or high-resolution images. If you encounter memory issues, try reducing the batch size or using gradient accumulation during fine-tuning.