The AMD RX 7900 XT is an excellent GPU for running the CLIP ViT-H/14 model due to its substantial 20GB of GDDR6 VRAM. CLIP ViT-H/14, in FP16 precision, requires only 2GB of VRAM, leaving a generous 18GB headroom. This allows for large batch sizes and concurrent execution of other tasks. The RX 7900 XT's 0.8 TB/s memory bandwidth ensures that data can be transferred quickly between the GPU and memory, preventing bottlenecks during inference. While the RX 7900 XT lacks dedicated Tensor Cores found in NVIDIA GPUs, its RDNA 3 architecture is optimized for compute tasks, enabling efficient processing of the model's matrix multiplications and other operations. The estimated 63 tokens/sec reflects the performance achievable with this setup.
For optimal performance, leverage inference frameworks optimized for AMD GPUs, such as ONNX Runtime or DirectML. Start with a batch size of 32 and monitor GPU utilization; increase it if VRAM allows to maximize throughput. Consider using mixed precision (FP16 or even BF16 if supported) to further accelerate inference without significant loss in accuracy. Regularly update your AMD drivers to benefit from performance improvements and bug fixes specific to AI workloads. Although quantization isn't strictly necessary given the ample VRAM, exploring INT8 quantization could further boost speed, but carefully evaluate the potential impact on accuracy.