The NVIDIA RTX 3080 12GB is an excellent GPU for running the BGE-Small-EN embedding model. The RTX 3080 boasts 12GB of GDDR6X VRAM, which far exceeds the model's modest 0.1GB VRAM requirement. This leaves a substantial 11.9GB VRAM headroom, ensuring smooth operation even with larger batch sizes or when running other applications concurrently. Furthermore, the RTX 3080's Ampere architecture, featuring 8960 CUDA cores and 280 Tensor Cores, provides significant computational power for efficient inference. The high memory bandwidth of 0.91 TB/s ensures rapid data transfer between the GPU and memory, minimizing potential bottlenecks during model execution.
Given the model's small size and the GPU's capabilities, users can expect low latency and high throughput. The estimated 90 tokens/sec provides a good starting point, but this can be further optimized. The architecture of the RTX 3080 is well-suited for FP16 inference, which is already the specified format for the model. The 350W TDP is a factor to consider for power and thermal management, but within standard operating parameters for a high-end GPU like the RTX 3080.
The RTX 3080 12GB is more than capable of handling BGE-Small-EN. Start with a batch size of 32, as suggested, and monitor GPU utilization. If utilization is low, gradually increase the batch size to maximize throughput. Experiment with different inference frameworks like ONNX Runtime or TensorRT for potential performance gains. Ensure that you have the latest NVIDIA drivers installed to leverage the full potential of the GPU's hardware acceleration capabilities.
Consider using techniques like quantization (if not already applied) to further reduce the model's memory footprint and potentially increase inference speed. While FP16 is a good starting point, explore INT8 quantization if supported by your chosen inference framework. Monitor GPU temperature and power consumption, especially when pushing the batch size to its limits, to ensure stable operation.