The NVIDIA RTX 4070, with its 12GB of GDDR6X VRAM and Ada Lovelace architecture, is exceptionally well-suited for running the BGE-M3 embedding model. BGE-M3, at only 0.5B parameters, requires a mere 1GB of VRAM when using FP16 precision. This leaves a substantial 11GB of VRAM headroom, ensuring smooth operation even with larger batch sizes or when running other applications concurrently. The RTX 4070's memory bandwidth of 0.5 TB/s further contributes to efficient data transfer, preventing memory bottlenecks during inference. The Ada Lovelace architecture provides optimized tensor cores which boost the performance of AI operations like those used in BGE-M3.
Given the ample VRAM available, users should prioritize maximizing batch size to improve throughput. Start with a batch size of 32 and experiment with increasing it until you observe diminishing returns in tokens/sec. Consider using inference frameworks like `llama.cpp` or `text-generation-inference` for further optimization. While FP16 offers a good balance of speed and accuracy, explore quantization techniques like INT8 to potentially further accelerate inference, especially if slight accuracy trade-offs are acceptable. Monitor GPU utilization to ensure you're fully leveraging the RTX 4070's capabilities.