The NVIDIA RTX 4070 Ti, with its 12GB of GDDR6X VRAM, is exceptionally well-suited for running the CLIP ViT-H/14 model. CLIP ViT-H/14, requiring only 2GB of VRAM in FP16 precision, leaves a substantial 10GB VRAM headroom. This ample VRAM allows for large batch sizes, enabling efficient parallel processing and higher throughput. The RTX 4070 Ti's 7680 CUDA cores and 240 Tensor Cores further accelerate the model's computations, particularly matrix multiplications inherent in vision transformers.
Furthermore, the RTX 4070 Ti's memory bandwidth of 0.5 TB/s ensures rapid data transfer between the GPU and its memory, preventing memory bottlenecks. This is crucial for maintaining high inference speeds, especially when processing multiple images or large batches. The Ada Lovelace architecture contributes to improved power efficiency and performance compared to previous generations, allowing for sustained high performance without excessive power consumption. Given these factors, the RTX 4070 Ti offers a robust and efficient platform for running CLIP ViT-H/14.
The estimated tokens/sec rate of 90 and batch size of 32 are reasonable expectations given the hardware specifications. Actual performance may vary slightly depending on the specific implementation and optimization techniques used.
For optimal performance with CLIP ViT-H/14 on the RTX 4070 Ti, leverage the available VRAM by experimenting with larger batch sizes. Start with a batch size of 32 and gradually increase it until you observe diminishing returns in terms of throughput or encounter VRAM limitations. Utilize TensorRT or other GPU acceleration libraries to further optimize inference speed. Consider using mixed precision (FP16) to reduce memory footprint and accelerate computations without significant loss in accuracy.
If you encounter any issues, such as excessive latency or out-of-memory errors, reduce the batch size or explore quantization techniques like INT8 to further minimize VRAM usage. Monitoring GPU utilization and temperature is also recommended to ensure the system is operating within safe and efficient parameters. If the application is latency-sensitive, consider optimizing the data pipeline to minimize data transfer overhead between the CPU and GPU.