The NVIDIA RTX 4060 Ti 8GB is an excellent match for running the BGE-Large-EN embedding model. With 8GB of GDDR6 VRAM, the RTX 4060 Ti comfortably exceeds the model's 0.7GB VRAM requirement, leaving a substantial 7.3GB headroom for larger batch sizes or running other applications concurrently. The Ada Lovelace architecture provides a good balance of compute and memory bandwidth (0.29 TB/s), allowing for efficient processing of embedding tasks. The 4352 CUDA cores and 136 Tensor cores further accelerate the matrix multiplications and other operations crucial for embedding generation.
BGE-Large-EN, being a relatively small model at 0.33B parameters, benefits from the RTX 4060 Ti's architecture. The model's modest context length of 512 tokens also contributes to its efficiency on this GPU. While higher memory bandwidth GPUs would yield faster performance, the RTX 4060 Ti strikes a good balance between cost and performance, making it a practical choice for many users. FP16 precision offers a good trade-off between speed and accuracy for this model, and is well-supported by the RTX 4060 Ti's Tensor Cores.
For optimal performance, utilize an inference framework like `llama.cpp` or `text-generation-inference` which are known for their efficient GPU utilization. Experiment with batch sizes, starting from the estimated 32, to maximize throughput without exceeding VRAM capacity. Monitoring GPU utilization is crucial; if the GPU is not fully utilized, increase the batch size. If you encounter VRAM limitations with other applications running, consider reducing the batch size or closing unnecessary programs.
While the RTX 4060 Ti handles BGE-Large-EN well in FP16, explore quantization techniques like INT8 or even INT4 (if supported by your chosen framework) for further performance gains, especially if you're running multiple instances of the model or have limited VRAM due to other processes. However, be mindful of potential accuracy trade-offs when using aggressive quantization.