The NVIDIA RTX 3080 10GB is an excellent GPU for running smaller AI models like BGE-Small-EN. With 10GB of GDDR6X VRAM and a memory bandwidth of 0.76 TB/s, it provides ample resources for this particular model, which only requires approximately 0.1GB of VRAM in FP16 precision. This leaves a substantial 9.9GB VRAM headroom, allowing for larger batch sizes or concurrent execution of multiple instances of the model. The RTX 3080's Ampere architecture, featuring 8704 CUDA cores and 272 Tensor Cores, ensures efficient computation for both inference and training tasks. The high memory bandwidth is crucial for quickly transferring data between the GPU and system memory, minimizing latency and maximizing throughput.
Given the large VRAM headroom, experiment with increasing the batch size to maximize GPU utilization and throughput. Start with a batch size of 32 and gradually increase it until you observe diminishing returns in terms of tokens/sec or encounter VRAM limitations. Consider using mixed precision inference (FP16 or even INT8 quantization) to potentially further improve performance without significant loss in accuracy. Monitor GPU utilization and memory usage to fine-tune the configuration for optimal performance. While the RTX 3080 is more than capable for BGE-Small-EN, future, more complex models might benefit from additional VRAM, so keep that in mind for long-term scalability.