The NVIDIA RTX 3080 12GB is an excellent GPU for running the BGE-M3 embedding model. With 12GB of GDDR6X VRAM, it far exceeds the model's 1GB requirement in FP16 precision. This substantial headroom ensures smooth operation, even with larger batch sizes and longer context lengths. The RTX 3080's Ampere architecture provides ample CUDA cores (8960) and Tensor Cores (280) to accelerate the model's computations, leveraging both parallel processing and optimized tensor operations.
Furthermore, the RTX 3080's high memory bandwidth of 0.91 TB/s is crucial for efficiently transferring data between the GPU and its memory. This is particularly important for models like BGE-M3 that require frequent memory access during inference. The combination of abundant VRAM and high memory bandwidth prevents bottlenecks, allowing the GPU to fully utilize its computational resources. The estimated 90 tokens/sec performance is a reasonable expectation, and might be further improved with optimized inference frameworks and quantization techniques.
To maximize performance, use an optimized inference framework such as `vLLM` or `text-generation-inference`. Experiment with different batch sizes to find the optimal balance between throughput and latency; a starting point of 32 is reasonable, but adjust based on your specific application and acceptable latency. While FP16 offers a good balance of speed and accuracy, consider experimenting with quantization techniques like INT8 to potentially further boost performance, though this may come with a slight reduction in accuracy.
Monitor GPU utilization and memory usage during inference. If you encounter performance bottlenecks, try reducing the batch size, shortening the context length, or employing more aggressive quantization. Ensure your NVIDIA drivers are up to date to take advantage of the latest performance optimizations. If you're primarily concerned with embedding generation speed and not text generation, consider using a dedicated embedding inference library.