The NVIDIA RTX 4070 Ti, with its 12GB of GDDR6X VRAM and Ada Lovelace architecture, offers ample resources for running the BGE-M3 embedding model. BGE-M3, at 0.5B parameters, requires approximately 1.0GB of VRAM when using FP16 precision. The 4070 Ti's substantial 12GB VRAM provides a significant headroom of 11GB, ensuring smooth operation even with larger batch sizes or concurrent tasks. Furthermore, the 4070 Ti's memory bandwidth of 0.5 TB/s ensures that data can be transferred quickly between the GPU and memory, minimizing bottlenecks during inference.
The RTX 4070 Ti is an excellent choice for running BGE-M3. Start with a batch size of 32 and a context length of 8192 tokens. Monitor GPU utilization and memory usage to fine-tune these parameters for optimal throughput. Consider using a framework like `llama.cpp` or `text-generation-inference` for efficient inference. If you encounter memory limitations with larger batch sizes or longer context lengths, explore quantization techniques like Q4_K_M or Q5_K_M to further reduce the model's memory footprint without significantly impacting performance. Always benchmark different quantization levels to find the best balance between speed and accuracy for your specific use case.