The NVIDIA RTX 4070 Ti, with its 12GB of GDDR6X VRAM, is exceptionally well-suited for running the BGE-Large-EN embedding model. BGE-Large-EN, at 0.33B parameters, has a relatively small memory footprint, requiring only 0.7GB of VRAM when using FP16 precision. This leaves a substantial 11.3GB VRAM headroom on the RTX 4070 Ti, ensuring that the model and associated operations can comfortably fit within the GPU's memory. The 4070 Ti's memory bandwidth of 0.5 TB/s further facilitates rapid data transfer between the GPU and memory, contributing to efficient model execution.
Furthermore, the RTX 4070 Ti's 7680 CUDA cores and 240 Tensor Cores are more than sufficient to handle the computational demands of BGE-Large-EN. While BGE-Large-EN isn't a computationally intensive model compared to larger language models, the Ada Lovelace architecture provides significant acceleration for inference tasks, especially when leveraging Tensor Cores for mixed-precision computations. This combination of ample VRAM, high memory bandwidth, and powerful processing cores results in excellent performance for BGE-Large-EN on the RTX 4070 Ti.
Given the substantial VRAM headroom and computational power of the RTX 4070 Ti, users should prioritize maximizing throughput by increasing the batch size during inference. Experiment with batch sizes up to 32 to find the optimal balance between latency and throughput for their specific application. Consider using a high-performance inference framework like vLLM or NVIDIA's TensorRT to further optimize performance. These frameworks can leverage techniques like dynamic batching and kernel fusion to accelerate inference.
Since the model comfortably fits within the available VRAM, users can also explore running multiple instances of the model concurrently to handle a higher volume of requests. Monitor GPU utilization to ensure that the GPU isn't bottlenecked and adjust the number of instances accordingly. If latency is a critical concern, consider reducing the batch size or exploring quantization techniques to further reduce the model's memory footprint and computational requirements.