The NVIDIA RTX 4070 SUPER, with its 12GB of GDDR6X VRAM and Ada Lovelace architecture, is exceptionally well-suited for running the BGE-Large-EN embedding model. BGE-Large-EN, being a relatively small model with only 0.33 billion parameters, requires approximately 0.7GB of VRAM when using FP16 precision. The RTX 4070 SUPER's ample VRAM provides a significant headroom of 11.3GB, ensuring that the model and its associated data structures fit comfortably within the GPU's memory. This eliminates potential bottlenecks related to memory swapping between the GPU and system RAM, which can severely impact performance.
Furthermore, the RTX 4070 SUPER's memory bandwidth of 0.5 TB/s ensures rapid data transfer between the GPU's processing units (CUDA and Tensor cores) and the VRAM. This is crucial for maintaining high throughput during inference, particularly when processing large batches of text. The 7168 CUDA cores and 224 Tensor cores contribute to accelerating the matrix multiplications and other computations that are fundamental to the BGE-Large-EN model. The Ada Lovelace architecture also incorporates advancements in Tensor Core technology, further enhancing the performance of AI workloads. Given these specifications, the RTX 4070 SUPER can handle BGE-Large-EN with ease, delivering high throughput and low latency.
For optimal performance with BGE-Large-EN on the RTX 4070 SUPER, it is recommended to utilize a high-performance inference framework such as vLLM or TensorRT. These frameworks are designed to optimize model execution on NVIDIA GPUs, leveraging features like kernel fusion and quantization to further improve throughput. Experiment with different batch sizes to find the sweet spot that maximizes GPU utilization without exceeding memory constraints. Starting with a batch size of 32 is a good baseline. Also, ensure you are using the latest NVIDIA drivers for optimal compatibility and performance.
Consider quantizing the model to INT8 or even INT4 if you need to reduce VRAM usage further or increase inference speed, although this might come at a slight cost in accuracy. If your application requires very low latency, experiment with smaller batch sizes. Monitor GPU utilization and memory usage to fine-tune the settings for your specific workload.