The NVIDIA RTX 3080 Ti, with its 12GB of GDDR6X VRAM and Ampere architecture, is exceptionally well-suited for running the BGE-Small-EN embedding model. BGE-Small-EN, being a relatively small model with only 0.03 billion parameters, requires a mere 0.1GB of VRAM in FP16 precision. This leaves a massive 11.9GB of VRAM headroom on the RTX 3080 Ti, allowing for substantial batch processing and concurrent execution of multiple instances of the model. The RTX 3080 Ti's 0.91 TB/s memory bandwidth further ensures rapid data transfer between the GPU and memory, minimizing potential bottlenecks during inference.
Furthermore, the RTX 3080 Ti's 10240 CUDA cores and 320 Tensor Cores provide ample computational power for the matrix multiplications and other operations inherent in embedding model inference. The Ampere architecture's optimizations for tensor operations, coupled with the high memory bandwidth, contribute to the model's expected performance of approximately 90 tokens per second. This combination of factors makes the RTX 3080 Ti an ideal platform for deploying BGE-Small-EN in various applications, from real-time semantic search to large-scale data analysis.
Given the ample VRAM headroom, you can maximize throughput by increasing the batch size. Start with a batch size of 32 and experiment with larger values until you observe diminishing returns or encounter memory limitations with other concurrent processes. Consider using mixed-precision inference (FP16 or even INT8 quantization) to further accelerate computation without significantly impacting accuracy. This is especially beneficial when handling large batches. Regularly monitor GPU utilization and memory usage to fine-tune your configuration for optimal performance.
For production deployments, explore using inference servers like NVIDIA Triton Inference Server or optimized frameworks like vLLM. These solutions provide features like dynamic batching, model management, and request queuing, which can significantly improve the overall efficiency and scalability of your BGE-Small-EN deployment. Profile your application to identify any potential bottlenecks and adjust settings accordingly.