The NVIDIA RTX 4060, with its 8GB of GDDR6 VRAM and Ada Lovelace architecture, is exceptionally well-suited for running the CLIP ViT-H/14 model. This vision model requires approximately 2GB of VRAM when using FP16 (half-precision floating point), leaving a substantial 6GB VRAM headroom. This ample VRAM allows for larger batch sizes and the potential to run other processes concurrently without encountering memory limitations. The RTX 4060's 3072 CUDA cores and 96 Tensor Cores further contribute to efficient computation, accelerating both the forward and backward passes during inference.
While VRAM is plentiful, the memory bandwidth of 0.27 TB/s could become a minor bottleneck at very large batch sizes. However, for typical usage scenarios, the memory bandwidth should be sufficient. The Ada Lovelace architecture also brings advancements in Tensor Core utilization, optimizing matrix multiplications which are fundamental to deep learning operations. Expect efficient processing, especially if leveraging TensorRT or other optimization libraries designed to exploit the hardware capabilities of the RTX 4060.
For optimal performance with CLIP ViT-H/14 on the RTX 4060, begin by using FP16 precision to maximize throughput and minimize VRAM usage. Experiment with increasing the batch size to fully utilize the available VRAM and improve tokens/sec. Consider using a framework like PyTorch or TensorFlow with CUDA support to leverage the GPU's parallel processing capabilities. If you encounter any performance bottlenecks, profile your code to identify the specific areas that are limiting speed.
If you intend to run multiple models simultaneously or have other VRAM-intensive tasks, monitor VRAM usage to ensure you don't exceed the 8GB limit. If necessary, reduce the batch size or consider using quantization techniques (e.g., INT8) to further minimize VRAM footprint, although this may come at the cost of slightly reduced accuracy.