The NVIDIA RTX 3090, with its substantial 24GB of GDDR6X VRAM, offers ample resources for running the CLIP ViT-L/14 model. This vision model, requiring only 1.5GB of VRAM in FP16 precision, leaves a significant 22.5GB of headroom. The RTX 3090's high memory bandwidth of 0.94 TB/s ensures rapid data transfer between the GPU and memory, which is crucial for minimizing latency during inference. Furthermore, the 10496 CUDA cores and 328 Tensor cores provide substantial computational power for accelerating the matrix multiplications and other operations inherent in the CLIP model. This combination of high VRAM, memory bandwidth, and compute resources makes the RTX 3090 an excellent choice for running CLIP ViT-L/14.
Given the abundant VRAM and computational resources of the RTX 3090, users should prioritize maximizing throughput by increasing the batch size. A batch size of 32 is a good starting point, but experimenting with even larger batch sizes may yield further performance improvements. Consider using TensorRT for optimized inference. Ensure the latest NVIDIA drivers are installed to take advantage of the latest performance optimizations. For optimal performance, use mixed precision (FP16) inference, which is well-supported by both the RTX 3090 and the CLIP model.