The NVIDIA RTX 4080 SUPER, boasting 16GB of GDDR6X VRAM and a memory bandwidth of 0.74 TB/s, is exceptionally well-suited for running the CLIP ViT-L/14 model. CLIP ViT-L/14, with its 0.4 billion parameters, demands approximately 1.5GB of VRAM when operating in FP16 precision. This leaves a substantial 14.5GB of VRAM headroom on the RTX 4080 SUPER, ensuring smooth operation even with larger batch sizes or when running alongside other processes. The Ada Lovelace architecture's 10240 CUDA cores and 320 Tensor cores further contribute to accelerating the model's computations, resulting in rapid inference speeds.
Given the ample VRAM and processing power, users can confidently run CLIP ViT-L/14 on the RTX 4080 SUPER without encountering memory limitations. To optimize performance, start with a batch size of 32 and gradually increase it until you observe diminishing returns in tokens/sec. Employing TensorRT for inference can further enhance speed and efficiency by optimizing the model for the RTX 4080 SUPER's architecture. If facing issues, reduce batch size or use mixed precision.