The NVIDIA RTX 4070, with its 12GB of GDDR6X VRAM, is an excellent match for running the CLIP ViT-H/14 model. This vision model, requiring only 2GB of VRAM in FP16 precision, leaves a substantial 10GB headroom. This abundant VRAM allows for larger batch sizes and potentially the concurrent execution of other tasks without memory constraints. The RTX 4070's Ada Lovelace architecture, featuring 5888 CUDA cores and 184 Tensor cores, provides ample computational power for accelerating the CLIP model's matrix multiplications and other tensor operations. Furthermore, the memory bandwidth of 0.5 TB/s ensures efficient data transfer between the GPU's memory and processing units, minimizing potential bottlenecks during inference.
For optimal performance with CLIP ViT-H/14 on the RTX 4070, start with a batch size of 32. Experiment with larger batch sizes to maximize GPU utilization, but monitor VRAM usage to avoid exceeding the available 12GB. Consider using TensorRT for further optimization, which can significantly improve inference speed. If encountering any performance limitations, explore quantization techniques like INT8 to reduce memory footprint and potentially increase throughput. Using the suggested inference framework can also improve performance.