The NVIDIA RTX 3060 Ti, with its 8GB of GDDR6 VRAM and Ampere architecture, is an excellent match for the BGE-M3 embedding model. BGE-M3, at 0.5B parameters, requires only 1GB of VRAM in FP16 precision. This leaves a substantial 7GB VRAM headroom, ensuring that the RTX 3060 Ti can comfortably load the model and handle reasonably large batch sizes without encountering memory limitations. The RTX 3060 Ti's 4864 CUDA cores and 152 Tensor Cores will contribute significantly to the model's inference speed, enabling real-time or near real-time embedding generation.
Given the ample VRAM available, users should prioritize maximizing batch size to improve throughput. Start with a batch size of 32 and experiment with larger values until performance plateaus or memory errors occur. Consider using inference frameworks like `llama.cpp` or `text-generation-inference` for optimized performance. While the model fits comfortably in FP16, exploring INT8 quantization could further boost inference speed with minimal impact on accuracy. Ensure you have the latest NVIDIA drivers installed for optimal performance.