The NVIDIA RTX 3060 12GB is an excellent match for the BGE-M3 embedding model. The RTX 3060 boasts 12GB of GDDR6 VRAM, significantly exceeding BGE-M3's modest 1GB requirement for FP16 precision. This leaves a substantial 11GB of headroom, allowing for larger batch sizes, longer context lengths, and concurrent execution of other tasks without encountering memory limitations. The Ampere architecture, with its 3584 CUDA cores and 112 Tensor Cores, provides ample computational power for efficient inference.
While the memory bandwidth of 0.36 TB/s is adequate, it's worth noting that higher bandwidth GPUs would further improve performance, especially with larger batch sizes. However, for typical embedding tasks, the RTX 3060 strikes a good balance between performance and cost. The estimated tokens/sec of 76 and a batch size of 32 are reasonable expectations given the model size and GPU capabilities. The Tensor Cores will accelerate the matrix multiplications inherent in the model, leading to faster inference times.
The RTX 3060 is well-suited for running BGE-M3, so users should focus on optimizing inference parameters to maximize throughput. Start with a batch size of 32 and experiment with increasing it until you observe diminishing returns or encounter memory constraints. Ensure you're using the latest NVIDIA drivers for optimal performance.
For further optimization, consider using a framework like `llama.cpp` or `text-generation-inference`, which are designed for efficient inference on NVIDIA GPUs. Quantization to INT8 might provide a slight speedup, but FP16 should be performant enough given the available VRAM. Monitor GPU utilization and memory usage to fine-tune settings for your specific workload.