The NVIDIA RTX 4080, with its 16GB of GDDR6X VRAM, is exceptionally well-suited for running the BGE-M3 embedding model. BGE-M3, at only 0.5 billion parameters, requires approximately 1GB of VRAM when using FP16 precision. This leaves a substantial 15GB of VRAM headroom on the RTX 4080, allowing for comfortable operation even with large batch sizes or when running other applications concurrently. The RTX 4080's ample memory bandwidth (0.72 TB/s) ensures that data can be transferred efficiently between the GPU and memory, minimizing potential bottlenecks during inference.
Given the comfortable VRAM headroom, users should experiment with larger batch sizes to maximize throughput. Start with a batch size of 32 and incrementally increase it until you observe performance degradation or run into memory limitations. Utilizing TensorRT for optimized inference can further enhance performance. For even faster inference, consider quantizing the model to INT8 or even INT4, although this may come at the cost of slightly reduced accuracy. Monitor GPU utilization to identify potential bottlenecks; if the GPU isn't fully utilized, increasing the batch size or using a more efficient inference engine may improve performance.