The NVIDIA RTX 4080, equipped with 16GB of GDDR6X VRAM, offers ample memory for running the CLIP ViT-H/14 model, which requires approximately 2GB of VRAM in FP16 precision. This leaves a significant VRAM headroom of 14GB, allowing for larger batch sizes and the potential to run multiple instances of the model concurrently, or to combine it with other models in a pipeline. The RTX 4080's memory bandwidth of 0.72 TB/s ensures fast data transfer between the GPU and memory, which is crucial for minimizing latency during inference. Furthermore, the presence of 9728 CUDA cores and 304 Tensor Cores within the Ada Lovelace architecture accelerate the matrix multiplications and other computations inherent in the CLIP model, leading to efficient processing.
Given the substantial VRAM headroom, users should experiment with increasing the batch size to maximize throughput. Start with a batch size of 32 and incrementally increase it until you observe diminishing returns in terms of tokens/sec or encounter memory constraints. Consider using TensorRT for optimized inference, which can further improve performance by leveraging the Tensor Cores on the RTX 4080. If memory becomes a bottleneck when running multiple models, explore quantization techniques such as INT8 to reduce the memory footprint of the CLIP model.