The NVIDIA RTX A4000, equipped with 16GB of GDDR6 VRAM and an Ampere architecture, offers ample resources for running the CLIP ViT-H/14 model. CLIP ViT-H/14, with its 0.6 billion parameters, requires approximately 2GB of VRAM when using FP16 precision. This leaves a substantial 14GB VRAM headroom on the A4000, ensuring smooth operation even with larger batch sizes or when running other processes concurrently. The A4000's memory bandwidth of 0.45 TB/s, coupled with its 6144 CUDA cores and 192 Tensor Cores, facilitates efficient data transfer and accelerated computations, resulting in responsive performance during inference.
Given the generous VRAM headroom, users can experiment with larger batch sizes to maximize throughput. Start with a batch size of 32 and gradually increase it until you observe diminishing returns in terms of tokens/sec. For optimal performance, consider using TensorRT for inference, as it can significantly accelerate the model's execution on NVIDIA GPUs. Alternatively, frameworks like vLLM offer efficient memory management and optimized kernels. Monitor GPU utilization and memory consumption to fine-tune the batch size and other parameters for the best balance between performance and resource usage.