The NVIDIA RTX 4080 SUPER is exceptionally well-suited for running the CLIP ViT-H/14 model. This GPU boasts 16GB of GDDR6X VRAM, while CLIP ViT-H/14, when operating in FP16 precision, only requires approximately 2GB. This leaves a significant 14GB VRAM headroom, allowing for substantial batch sizes and the potential to run multiple instances of the model concurrently or alongside other applications without encountering memory constraints. Furthermore, the RTX 4080 SUPER's memory bandwidth of 0.74 TB/s ensures rapid data transfer between the GPU and memory, crucial for minimizing latency during inference.
The RTX 4080 SUPER's Ada Lovelace architecture, featuring 10240 CUDA cores and 320 Tensor cores, provides ample computational power for the matrix multiplications and other operations inherent in the CLIP ViT-H/14 model. The Tensor Cores, specifically designed for accelerating deep learning workloads, contribute significantly to the model's inference speed. The estimated tokens/second rate of 90 and a batch size of 32 are indicative of the performance one can expect from this pairing, showcasing the RTX 4080 SUPER's capability to handle this model with ease.
Given the ample VRAM and computational power of the RTX 4080 SUPER, users should prioritize maximizing batch size to increase throughput. Experimenting with different inference frameworks like vLLM or text-generation-inference could further optimize performance. While FP16 precision is sufficient for CLIP ViT-H/14, explore lower precision quantization methods (e.g., INT8) if even higher throughput is desired, keeping in mind the potential impact on accuracy.
If encountering performance bottlenecks, ensure that the GPU drivers are up-to-date and that the system is not CPU-bound. Monitoring GPU utilization and memory usage during inference can help identify potential areas for optimization. For production deployments, consider using a dedicated inference server to manage resources and scale the model efficiently.