The NVIDIA RTX 4060 Ti 16GB is an excellent match for running the BGE-Large-EN embedding model. BGE-Large-EN, with its 0.33B parameters, requires approximately 0.7GB of VRAM when using FP16 precision. The RTX 4060 Ti's substantial 16GB of GDDR6 VRAM provides a significant headroom of 15.3GB. This ample VRAM allows for comfortable operation, accommodating larger batch sizes and potentially enabling the concurrent execution of other tasks without memory constraints.
While VRAM is plentiful, the RTX 4060 Ti's memory bandwidth of 0.29 TB/s is a factor to consider. Although sufficient for BGE-Large-EN, maximizing throughput might require careful optimization of batch sizes and inference frameworks. The 4352 CUDA cores and 136 Tensor cores within the Ada Lovelace architecture contribute to efficient computation, enabling respectable inference speeds. Expect approximately 76 tokens per second, a solid performance level for many embedding-related applications. The 165W TDP suggests efficient power usage for the performance delivered.
For optimal performance, leverage an inference framework like `llama.cpp` or `vLLM`, which are known for their efficiency and optimization capabilities. Start with a batch size of 32, as this is a good starting point for balancing throughput and latency. Monitor VRAM usage and adjust batch sizes accordingly. Experiment with different context lengths, though the model's native 512 tokens should work well. Consider using mixed precision (FP16 or even INT8 quantization if supported by your chosen framework) to further improve performance without significant accuracy loss. Profile your application to identify any bottlenecks and fine-tune parameters for the best results.
If you encounter performance limitations, explore reducing the batch size or using a more aggressive quantization technique like INT8. Ensure that your drivers are up to date for optimal compatibility and performance. For demanding applications, consider a higher-end GPU with more memory bandwidth, although the RTX 4060 Ti 16GB should be more than adequate for most BGE-Large-EN use cases.