The NVIDIA RTX 3060 12GB is exceptionally well-suited for running the BGE-Small-EN embedding model. BGE-Small-EN, with its 0.03B parameters, requires a mere 0.1GB of VRAM when using FP16 precision. The RTX 3060's 12GB of GDDR6 VRAM provides an enormous headroom of 11.9GB, ensuring that VRAM constraints will not be a limiting factor. The Ampere architecture of the RTX 3060, coupled with its 3584 CUDA cores and 112 Tensor cores, allows for efficient computation of the model's operations, facilitating rapid inference.
While VRAM is plentiful, the RTX 3060's memory bandwidth of 0.36 TB/s will influence the overall throughput. This bandwidth is sufficient to support the relatively small size of BGE-Small-EN, but it's important to consider when scaling up batch sizes or using more complex models concurrently. The expected performance of 76 tokens/sec and a batch size of 32 are reasonable estimates given the model and hardware specifications. These figures can vary based on the specific inference framework and optimization techniques employed.
Given the RTX 3060's capabilities and the BGE-Small-EN's modest requirements, you should prioritize maximizing throughput and efficiency. Start by experimenting with larger batch sizes to fully utilize the GPU's processing power. Explore different inference frameworks like ONNX Runtime or TensorRT to potentially optimize performance further. Consider using mixed precision (e.g., FP16 or even INT8 quantization) if you observe any performance bottlenecks, though it's unlikely to be necessary given the ample VRAM and compute available.
If you encounter unexpected performance issues, ensure that your drivers are up-to-date and that the GPU is properly configured for compute workloads. Monitor GPU utilization and memory usage to identify any potential bottlenecks. If you plan to run multiple models concurrently, carefully manage VRAM allocation to avoid exceeding the GPU's capacity.