The NVIDIA RTX 3090, with its substantial 24GB of GDDR6X VRAM, is exceptionally well-suited for running the BGE-M3 embedding model. BGE-M3, at only 0.5B parameters, requires a mere 1GB of VRAM in FP16 precision, leaving a massive 23GB of headroom. This abundant VRAM allows for large batch sizes and the potential to run multiple instances of the model concurrently, significantly boosting throughput. The RTX 3090's high memory bandwidth of 0.94 TB/s ensures rapid data transfer between the GPU and memory, preventing memory bottlenecks that can hinder performance, especially during large batch inferences.
Given the RTX 3090's ample resources, users should prioritize maximizing batch size to fully utilize the GPU's parallel processing capabilities. Experiment with batch sizes up to 32 or even higher, monitoring GPU utilization to find the optimal balance between throughput and latency. Consider using inference frameworks like `vLLM` or `text-generation-inference` to take advantage of optimized kernel implementations and advanced scheduling techniques that can further improve performance. Quantization to INT8 or even lower precisions could further reduce VRAM footprint, allowing for even larger batch sizes without sacrificing accuracy significantly.