The NVIDIA RTX 4060 Ti 16GB is an excellent match for the BGE-M3 embedding model. BGE-M3, with its modest 0.5B parameters, requires only 1.0GB of VRAM when using FP16 precision. The RTX 4060 Ti's ample 16GB of GDDR6 VRAM provides a substantial 15GB of headroom, ensuring smooth operation even with larger batch sizes or when running other applications concurrently. The card's 290 GB/s memory bandwidth is also sufficient to handle the data transfer demands of this relatively small model, preventing memory bandwidth from becoming a bottleneck.
Furthermore, the RTX 4060 Ti's Ada Lovelace architecture brings considerable advantages. Its 4352 CUDA cores and 136 Tensor cores significantly accelerate the matrix multiplications and other tensor operations crucial for efficient model inference. We estimate a throughput of approximately 76 tokens per second, which is a respectable speed for many embedding tasks. The 165W TDP of the RTX 4060 Ti is also reasonable, allowing it to be used in a wide range of desktop systems without requiring an excessively large power supply.
The RTX 4060 Ti 16GB and BGE-M3 combination is well-suited for a variety of embedding tasks. To maximize performance, start with a batch size of 32 and a context length of 8192 tokens. Experiment with different inference frameworks such as `llama.cpp` or `text-generation-inference` to see which offers the best performance for your specific application. Monitor GPU utilization and memory usage to ensure you're not bottlenecked by other system components.
While FP16 precision is sufficient for BGE-M3 and provides good performance, consider experimenting with INT8 quantization for further speed improvements if accuracy loss is acceptable for your use case. If you encounter VRAM limitations when running other applications simultaneously, reduce the batch size or close unnecessary programs.