The NVIDIA H100 SXM, with its substantial 80GB of HBM3 memory and Hopper architecture, is exceptionally well-suited for running the CLIP ViT-L/14 model. CLIP ViT-L/14, requiring only 1.5GB of VRAM in FP16 precision, leaves a significant 78.5GB of headroom. This ample VRAM allows for large batch sizes, which are crucial for maximizing GPU utilization and throughput. The H100's high memory bandwidth (3.35 TB/s) ensures rapid data transfer between the GPU and memory, preventing bottlenecks during inference.
Furthermore, the H100's 16896 CUDA cores and 528 Tensor Cores provide immense parallel processing power. The Tensor Cores, specifically designed for matrix multiplication operations common in deep learning, will significantly accelerate CLIP ViT-L/14's computations. The Hopper architecture introduces features like the Transformer Engine, further optimizing performance for transformer-based models like CLIP. This combination of high memory capacity, bandwidth, and compute power results in excellent performance, enabling high throughput and low latency inference.
Given the H100's capabilities, prioritize maximizing batch size to fully utilize the GPU. Experiment with different batch sizes, starting from the estimated 32, to find the optimal balance between throughput and latency for your specific application. Consider using mixed precision (FP16 or even BF16) to further accelerate inference without significant accuracy loss. Regularly monitor GPU utilization and memory usage to identify potential bottlenecks and adjust settings accordingly.
For deployment, leverage optimized inference frameworks like NVIDIA Triton Inference Server or vLLM to streamline the serving process and further improve performance. These frameworks offer features like dynamic batching and model optimization, which can enhance throughput and reduce latency. If you are dealing with a high volume of requests, consider using multiple instances of the model to distribute the workload across the GPU's resources.