The NVIDIA RTX 3080 10GB is an excellent GPU for running the BGE-Large-EN embedding model. With 10GB of GDDR6X VRAM and a memory bandwidth of 0.76 TB/s, it offers ample resources for this relatively small model. BGE-Large-EN, with its 0.33 billion parameters, requires only 0.7GB of VRAM when using FP16 precision. This leaves a significant 9.3GB of VRAM headroom, allowing for larger batch sizes and potentially the concurrent execution of other tasks without encountering memory limitations. The RTX 3080's Ampere architecture, featuring 8704 CUDA cores and 272 Tensor cores, is well-suited for the matrix multiplications inherent in transformer models like BGE-Large-EN, contributing to efficient and fast inference.
Given the substantial VRAM headroom, experiment with increasing the batch size to maximize throughput. A batch size of 32 is a good starting point, but you may be able to push it higher depending on the specific application and latency requirements. While FP16 precision is sufficient for BGE-Large-EN, consider using TensorRT for further optimization. TensorRT can perform graph optimizations and quantization (if needed) to squeeze even more performance out of the RTX 3080. Monitor GPU utilization and memory usage to ensure optimal performance and prevent bottlenecks.