The NVIDIA RTX 3070 Ti, with its 8GB of GDDR6X VRAM and Ampere architecture, is an excellent match for the BGE-Small-EN embedding model. BGE-Small-EN is a compact model with only 0.03 billion parameters, requiring a mere 0.1GB of VRAM when using FP16 precision. This leaves a substantial 7.9GB of VRAM headroom, ensuring that the model and associated inference operations can easily fit within the GPU's memory. The RTX 3070 Ti's memory bandwidth of 0.61 TB/s is also more than sufficient for handling the data transfer requirements of this relatively small model, preventing memory bandwidth from becoming a bottleneck.
Furthermore, the RTX 3070 Ti boasts 6144 CUDA cores and 192 Tensor cores, which significantly accelerate the matrix multiplications and other computations inherent in neural network inference. The Ampere architecture's improvements in Tensor Core utilization contribute to faster processing times. Given the model's small size, the expected throughput is high, enabling real-time or near-real-time embedding generation. Expect efficient utilization of the GPU's resources leading to low latency and high throughput.
For optimal performance with BGE-Small-EN on the RTX 3070 Ti, leverage a high-performance inference framework like vLLM or NVIDIA's TensorRT. These frameworks are designed to maximize GPU utilization and minimize latency. Experiment with different batch sizes to find the sweet spot for your application. Starting with a batch size of 32 is a good baseline, but you might be able to increase it further without significantly impacting latency, thereby increasing overall throughput.
Consider using quantization techniques, such as INT8, to further reduce memory footprint and potentially increase inference speed, although the benefits may be minimal given the model's already small size. Profile your application to identify any bottlenecks and adjust settings accordingly. Monitor GPU utilization and memory usage to ensure that you are fully leveraging the RTX 3070 Ti's capabilities.