The NVIDIA RTX 4070 Ti, with its 12GB of GDDR6X VRAM and Ada Lovelace architecture, offers ample resources 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 substantial 11.9GB of VRAM headroom, ensuring smooth operation without memory constraints. The RTX 4070 Ti's 0.5 TB/s memory bandwidth further contributes to efficient data transfer between the GPU and memory, minimizing potential bottlenecks during inference.
Furthermore, the RTX 4070 Ti's 7680 CUDA cores and 240 Tensor Cores provide significant computational power for accelerating the matrix multiplications and other operations inherent in deep learning models. This translates to excellent performance for BGE-Small-EN, allowing for high throughput and low latency. The estimated 90 tokens/sec and a batch size of 32 are achievable due to the model's small size and the GPU's robust capabilities. The Ada Lovelace architecture also brings advancements in Tensor Core performance, further boosting the model's efficiency.
Given the RTX 4070 Ti's generous VRAM and processing power, users can confidently run BGE-Small-EN with default settings and expect excellent performance. Experimenting with larger batch sizes (up to the suggested 32) can further optimize throughput, especially when processing multiple embeddings simultaneously. Consider using a suitable inference framework like `vLLM` or `text-generation-inference` to leverage optimized kernels and memory management for maximum efficiency. If encountering issues, verify driver versions and ensure the correct CUDA toolkit is installed.
For even higher throughput, especially in production environments, consider using quantization techniques like INT8 or even smaller precisions if supported by the inference framework. This can potentially double the throughput without significantly impacting embedding quality. However, always evaluate the trade-off between performance and accuracy when applying quantization.