The NVIDIA RTX 3080 Ti, with its 12GB of GDDR6X VRAM and Ampere architecture, is exceptionally well-suited for running the BGE-Large-EN embedding model. BGE-Large-EN, at 0.33B parameters, has a relatively small memory footprint, requiring only 0.7GB of VRAM in FP16 precision. This leaves a substantial 11.3GB VRAM headroom on the RTX 3080 Ti, allowing for large batch sizes and concurrent execution of multiple instances of the model without encountering memory limitations. The RTX 3080 Ti's high memory bandwidth (0.91 TB/s) ensures rapid data transfer between the GPU and memory, preventing bottlenecks during inference. The 10240 CUDA cores and 320 Tensor Cores will further accelerate the computations required for the embedding generation.
Given the ample VRAM available, users should prioritize maximizing throughput by experimenting with larger batch sizes. Start with a batch size of 32 and gradually increase it until performance plateaus or VRAM usage approaches the limit. Utilizing TensorRT or other GPU acceleration libraries can further optimize inference speed. For real-time applications, consider using techniques like request batching to amortize the overhead of model loading and inference. Monitoring GPU utilization is crucial to identify potential bottlenecks and fine-tune settings for optimal performance. Consider using a benchmark to measure tokens/sec for different batch sizes and context lengths to find the optimal configuration.