The NVIDIA RTX 4070 SUPER, with its 12GB of GDDR6X VRAM and Ada Lovelace architecture, is exceptionally well-suited for running the BGE-Small-EN embedding model. BGE-Small-EN's tiny 0.03B parameter size translates to a minimal 0.1GB VRAM footprint in FP16 precision. This leaves a massive 11.9GB VRAM headroom, ensuring smooth operation even with large batch sizes and parallel processing. The RTX 4070 SUPER's 0.5 TB/s memory bandwidth further contributes to efficient data transfer, preventing memory bottlenecks during inference. The 7168 CUDA cores and 224 Tensor cores provide ample computational power for rapid embedding generation.
The Ada Lovelace architecture's advancements in tensor core utilization are particularly beneficial for embedding models like BGE-Small-EN. The combination of abundant VRAM, high memory bandwidth, and powerful CUDA/Tensor cores ensures that the RTX 4070 SUPER can handle BGE-Small-EN with ease, achieving high throughput and low latency. This setup allows for real-time embedding generation, making it ideal for applications like semantic search, document retrieval, and text classification.
Given the specifications, the estimated tokens/sec of 90 and a batch size of 32 are reasonable starting points. However, these numbers can likely be significantly improved with optimization, especially by exploring different inference frameworks and quantization techniques.
For optimal performance with BGE-Small-EN on the RTX 4070 SUPER, begin by using a framework like ONNX Runtime, Hugging Face Transformers, or TensorRT. Experiment with different batch sizes to find the sweet spot that maximizes throughput without exceeding VRAM capacity. Given the model's small size, you could even experiment with running multiple instances of the model concurrently to further increase throughput.
While FP16 offers a good balance of speed and accuracy, consider exploring quantization techniques like INT8 or even smaller bit widths if you need to further reduce VRAM usage or increase inference speed. However, be mindful of potential accuracy degradation when using lower precision formats. Profile your application to identify any bottlenecks and fine-tune your settings accordingly. Also, ensure your drivers are up to date for the best possible performance.