The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, is exceptionally well-suited for running the CLIP ViT-H/14 model. CLIP ViT-H/14 requires only 2GB of VRAM in FP16 precision, leaving a substantial 22GB of headroom on the 3090 Ti. This abundant VRAM allows for large batch sizes and the potential to run multiple instances of the model concurrently. The 3090 Ti's high memory bandwidth (1.01 TB/s) ensures efficient data transfer between the GPU and memory, minimizing bottlenecks during inference. Furthermore, the 10752 CUDA cores and 336 Tensor Cores within the Ampere architecture provide significant computational power for accelerating the matrix multiplications and other operations crucial to CLIP's performance.
The ample VRAM headroom translates to the ability to experiment with larger batch sizes without encountering out-of-memory errors. A larger batch size can improve throughput, as the GPU can process more images in parallel. The Ampere architecture's Tensor Cores are specifically designed to accelerate mixed-precision computations, further boosting performance. The estimated 90 tokens/sec is a reasonable expectation given the model size and GPU capabilities, but actual performance will vary depending on factors such as the specific input images, the software framework used, and any optimizations applied. The estimated batch size of 32 is a good starting point for experimentation, but may be further increased if VRAM allows.
For optimal performance with CLIP ViT-H/14 on the RTX 3090 Ti, leverage a framework like PyTorch or TensorFlow with CUDA support to fully utilize the GPU's capabilities. Start with a batch size of 32 and gradually increase it until you reach the VRAM limit or observe diminishing returns in throughput. Experiment with different optimization techniques such as mixed-precision inference (FP16) to further improve speed. Consider using libraries like NVIDIA TensorRT for model optimization and deployment, which can significantly enhance inference performance.
If you encounter performance bottlenecks, profile your code to identify the most time-consuming operations. Ensure that your data loading pipeline is efficient to avoid starving the GPU. For even higher throughput, explore techniques like model parallelism across multiple GPUs, although this is likely unnecessary for CLIP ViT-H/14 on a single RTX 3090 Ti due to its relatively small size.