The NVIDIA RTX 3080 10GB is an excellent GPU for running the CLIP ViT-H/14 model. The RTX 3080's 10GB of GDDR6X VRAM provides ample space for the model, which requires only 2.0GB in FP16 precision. This leaves a significant VRAM headroom of 8.0GB, allowing for larger batch sizes and potentially the simultaneous operation of other tasks. The Ampere architecture of the RTX 3080, with its 8704 CUDA cores and 272 Tensor cores, is well-suited for the matrix multiplications and other computations inherent in deep learning inference. The high memory bandwidth of 0.76 TB/s ensures that data can be moved efficiently between the GPU and memory, minimizing bottlenecks during inference.
For optimal performance with the CLIP ViT-H/14 model on the RTX 3080, start with a batch size of 32. Monitor GPU utilization and memory usage to determine if you can increase the batch size further without exceeding VRAM limits or significantly impacting latency. Consider using TensorRT for optimized inference, as it can leverage the Tensor Cores on the RTX 3080 to accelerate computations. If you encounter any memory issues, try reducing the batch size or using a lower precision format like INT8, although this may slightly impact accuracy.