The NVIDIA RTX 4060, equipped with 8GB of GDDR6 VRAM and based on the Ada Lovelace architecture, is exceptionally well-suited for running the BGE-Small-EN embedding model. BGE-Small-EN, with its modest 0.03B parameters, requires only 0.1GB of VRAM when using FP16 precision. This leaves a substantial 7.9GB of VRAM headroom, ensuring that the model can operate comfortably even with larger batch sizes or when integrated into more complex applications. The RTX 4060's 3072 CUDA cores and 96 Tensor cores further contribute to its ability to efficiently process the model's computations.
While the RTX 4060's memory bandwidth of 0.27 TB/s isn't the highest available, it's more than sufficient for a model of this size. The estimated tokens/sec of 76 and a batch size of 32 indicate good performance, making it suitable for real-time applications or high-throughput processing. The Ada Lovelace architecture also incorporates advancements in tensor core utilization, which can further accelerate the embedding generation process. Overall, the RTX 4060 provides a balanced and efficient platform for deploying BGE-Small-EN.
For optimal performance with BGE-Small-EN on the RTX 4060, start with a batch size of 32 and a context length of 512 tokens. Monitor VRAM usage and adjust the batch size accordingly to maximize throughput without exceeding available memory. Experiment with different inference frameworks like ONNX Runtime or TensorRT to potentially further improve performance.
Consider using quantization techniques, such as INT8, to reduce the model's memory footprint and potentially increase inference speed, although this might come at a slight accuracy cost. Ensure that the NVIDIA drivers are up-to-date to benefit from the latest performance optimizations for the Ada Lovelace architecture.