The NVIDIA RTX 3080 Ti, with its 12GB of GDDR6X VRAM and Ampere architecture, is an excellent choice for running the BGE-M3 embedding model. BGE-M3, being a relatively small model with only 0.5 billion parameters, requires approximately 1GB of VRAM when using FP16 precision. This leaves a substantial 11GB VRAM headroom on the RTX 3080 Ti, ensuring smooth operation even with larger batch sizes or when running other applications concurrently. The RTX 3080 Ti's memory bandwidth of 0.91 TB/s further contributes to efficient data transfer, preventing memory bottlenecks during inference.
For optimal performance with BGE-M3 on the RTX 3080 Ti, start with a batch size of 32 and a context length of 8192 tokens. Monitor GPU utilization and memory usage to fine-tune these parameters. Consider using a high-performance inference framework like `vLLM` or `text-generation-inference` to leverage the GPU's capabilities fully. If you encounter VRAM constraints when experimenting with larger models, explore quantization techniques such as INT8 to reduce memory footprint without significant performance degradation. Regularly update your NVIDIA drivers to ensure compatibility and access the latest performance optimizations.