The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and Ada Lovelace architecture, offers substantial resources for running AI models. The BGE-Large-EN embedding model, at 0.33B parameters, requires only 0.7GB of VRAM when using FP16 precision. This leaves a significant VRAM headroom of 23.3GB, indicating that the RTX 4090 is more than capable of handling this model, even with large batch sizes or when running multiple instances concurrently.
Furthermore, the RTX 4090's memory bandwidth of 1.01 TB/s ensures rapid data transfer between the GPU and its memory, preventing bottlenecks during inference. The 16384 CUDA cores and 512 Tensor cores accelerate the matrix multiplications and other computations essential for deep learning, leading to high throughput. The estimated 90 tokens/second performance is a reasonable expectation, but actual performance may vary depending on the specific inference framework and optimization techniques employed. The architecture's efficiency also contributes to managing the 450W TDP effectively, although proper cooling is still essential.
Given the ample VRAM available, users should experiment with larger batch sizes (up to 32 or even higher) to maximize GPU utilization and throughput. Consider using an optimized inference framework such as vLLM or TensorRT to further improve performance. These frameworks often incorporate techniques like kernel fusion and quantization to reduce latency and increase tokens/second. Monitoring GPU utilization and memory usage is crucial to identify potential bottlenecks and fine-tune settings for optimal performance.
While FP16 precision is sufficient for BGE-Large-EN, users can explore lower precision formats like INT8 or even INT4 quantization to further reduce memory footprint and potentially increase inference speed. However, it's essential to evaluate the impact of quantization on the model's accuracy and ensure that the resulting embeddings still meet the application's requirements. If experiencing any issues, revert to FP16 and optimize batch sizes and context length.