The NVIDIA RTX 4000 Ada, with its 20GB of GDDR6 VRAM, offers ample resources for running the CLIP ViT-L/14 model. CLIP ViT-L/14, requiring only 1.5GB of VRAM in FP16 precision, fits comfortably within the GPU's memory capacity, leaving a significant 18.5GB headroom. This substantial VRAM surplus allows for larger batch sizes and the potential to run multiple instances of the model concurrently. The RTX 4000 Ada's 0.36 TB/s memory bandwidth, coupled with its 6144 CUDA cores and 192 Tensor cores, ensures efficient data transfer and accelerated computations, contributing to faster inference speeds. The Ada Lovelace architecture further enhances performance through optimized tensor operations and improved memory management.
Given the generous VRAM headroom, users should explore increasing the batch size to maximize throughput. Experiment with batch sizes up to 32 to find the optimal balance between latency and throughput. Employing TensorRT for inference can further optimize performance by leveraging the Tensor Cores on the RTX 4000 Ada. Consider using mixed precision (FP16 or even INT8 with quantization-aware training) to further reduce memory footprint and accelerate computations, although this may require fine-tuning to maintain accuracy. Monitor GPU utilization to ensure the model is effectively leveraging the available resources.