The NVIDIA RTX 4000 Ada, with its 20GB of GDDR6 VRAM, is exceptionally well-suited for running the BGE-Small-EN embedding model. BGE-Small-EN's tiny 0.03B parameter size translates to a mere 0.1GB VRAM footprint when using FP16 precision. This leaves a substantial 19.9GB of VRAM headroom, allowing for large batch sizes and concurrent execution of multiple instances of the model. The RTX 4000 Ada's 360 GB/s memory bandwidth, while not the highest available, is more than sufficient for the computational demands of such a small model, ensuring efficient data transfer between the GPU and memory.
The Ada Lovelace architecture's 6144 CUDA cores and 192 Tensor Cores provide ample computational resources for accelerating the matrix multiplications and other operations inherent in embedding generation. The Tensor Cores, in particular, are optimized for FP16 operations, leading to significant performance gains. Given the model's modest size and the GPU's capabilities, users can expect high throughput, processing around 90 tokens per second. This combination of low memory requirements and robust computational power makes the RTX 4000 Ada an ideal platform for deploying BGE-Small-EN in real-world applications.
Given the ample VRAM headroom, experiment with increasing the batch size to maximize throughput. Start with a batch size of 32 and gradually increase it until you observe diminishing returns in terms of tokens processed per second or encounter memory limitations. Consider using a dedicated inference framework like ONNX Runtime or TensorRT to further optimize performance. While FP16 precision is sufficient for most use cases with BGE-Small-EN, you could explore INT8 quantization for even faster inference, although this may come at a slight cost in accuracy. Monitor GPU utilization to ensure the model is fully leveraging the RTX 4000 Ada's resources; if utilization is low, it may indicate a bottleneck elsewhere in your pipeline, such as data loading or pre-processing.