The NVIDIA RTX 3080 12GB, with its Ampere architecture, 8960 CUDA cores, and 12GB of GDDR6X VRAM, is well-suited for running the CLIP ViT-H/14 model. CLIP ViT-H/14 requires approximately 2GB of VRAM when using FP16 precision. The RTX 3080's 12GB VRAM provides ample headroom (10GB), eliminating potential out-of-memory errors and allowing for larger batch sizes. The RTX 3080's memory bandwidth of 0.91 TB/s ensures that data can be transferred efficiently between the GPU and memory, which is crucial for inference speed.
The Ampere architecture's Tensor Cores further accelerate the matrix multiplications inherent in deep learning models like CLIP, leading to faster inference times compared to GPUs without dedicated Tensor Cores. The RTX 3080's power consumption (350W TDP) should be considered, ensuring adequate cooling and power supply are available. The estimated 90 tokens/sec is an approximation, and actual performance may vary depending on the specific implementation, batch size, and other system configurations. Larger batch sizes can improve throughput but may also increase latency.
The RTX 3080 12GB is an excellent choice for running CLIP ViT-H/14. Start with a batch size of 32 and monitor GPU utilization. If utilization is low, consider increasing the batch size to further improve throughput. Experiment with different inference frameworks to find the optimal balance between speed and memory usage. For further optimization, explore quantization techniques like INT8, but be aware that this might slightly impact accuracy. Ensure you have the latest NVIDIA drivers installed for optimal performance and compatibility.
If you encounter performance bottlenecks, investigate CPU usage, as data preprocessing and post-processing can sometimes become limiting factors. Consider offloading some of these tasks to the GPU if possible. Monitor GPU temperature to prevent thermal throttling, which can significantly reduce performance.