The NVIDIA RTX 4060 Ti 8GB is an excellent GPU choice for running the BGE-M3 embedding model. With 8GB of GDDR6 VRAM, it comfortably exceeds the 1.0GB VRAM requirement of BGE-M3, leaving a substantial 7GB headroom for larger batch sizes, longer context lengths, or running other applications concurrently. The RTX 4060 Ti's Ada Lovelace architecture, featuring 4352 CUDA cores and 136 Tensor cores, provides ample computational power for efficient inference. The memory bandwidth of 0.29 TB/s is sufficient for loading model weights and processing input data, although higher bandwidth would further improve performance.
BGE-M3's relatively small size (0.5B parameters) means that it can fit entirely within the GPU's memory, minimizing data transfer between the GPU and system RAM. This is crucial for achieving low latency and high throughput. The estimated tokens/sec of 76 and a suggested batch size of 32 indicate a responsive and efficient inference performance. FP16 precision is well-suited for this model on this GPU, balancing speed and accuracy. The Ada Lovelace architecture's Tensor Cores accelerate FP16 operations, further boosting performance.
For optimal performance with the RTX 4060 Ti and BGE-M3, utilize a framework like `llama.cpp` with GPU acceleration or `text-generation-inference` for optimized serving. Start with a batch size of 32 and a context length of 8192 tokens, and then experiment to find the sweet spot between throughput and latency based on your specific application. Monitor GPU utilization and memory usage to ensure you're not bottlenecked by other processes. Consider using mixed precision inference (e.g., bfloat16) if your framework supports it for a potential performance boost, but benchmark carefully to ensure accuracy isn't significantly affected.
If you encounter memory limitations with larger batch sizes or longer context lengths, consider using quantization techniques (e.g., Q4 or Q8) to reduce the model's memory footprint. However, be aware that quantization can sometimes impact accuracy, so thorough evaluation is essential. For production deployments, explore using tools like TensorRT to further optimize the model for inference on NVIDIA GPUs.