The NVIDIA RTX 4070 Ti SUPER, equipped with 16GB of GDDR6X VRAM and an Ada Lovelace architecture, provides substantial resources for running the BGE-M3 embedding model. BGE-M3, with its relatively small 0.5 billion parameters, requires only 1GB of VRAM in FP16 precision. This leaves a significant 15GB VRAM headroom, ensuring comfortable operation even with larger batch sizes or when combined with other processes utilizing the GPU. The 4070 Ti SUPER's memory bandwidth of 0.67 TB/s is more than sufficient to feed data to the model, preventing memory bandwidth from becoming a bottleneck during inference.
Given the ample VRAM headroom, users can experiment with larger batch sizes to maximize throughput. Start with a batch size of 32 and gradually increase it while monitoring GPU utilization and latency. Consider using a high-performance inference framework like vLLM or TensorRT to further optimize performance. While BGE-M3 is already a compact model, explore quantization techniques (e.g., INT8) for potential speed improvements with minimal accuracy loss. Monitor the temperature of your GPU, especially when running sustained inference workloads, to ensure optimal performance and longevity.