The NVIDIA RTX 4090, with its massive 24GB of GDDR6X VRAM, offers ample resources for running the BGE-Small-EN embedding model. BGE-Small-EN, a relatively small model with only 0.03 billion parameters, requires a mere 0.1GB of VRAM when using FP16 precision. This leaves a substantial 23.9GB of VRAM headroom, ensuring that memory constraints won't be a bottleneck. The RTX 4090's impressive 1.01 TB/s memory bandwidth further facilitates rapid data transfer between the GPU and memory, crucial for efficient model execution. The combination of abundant VRAM and high memory bandwidth allows for high throughput during inference.
Given the RTX 4090's capabilities and the BGE-Small-EN's modest requirements, you can maximize throughput by increasing the batch size during inference. Experiment with batch sizes up to 32 or even higher to fully utilize the GPU's parallel processing power. Explore inference frameworks like vLLM or Text Generation Inference, which are designed to optimize performance for large language models and may offer additional speed improvements. Consider using mixed precision (FP16 or even BF16) for further acceleration, although the model is already small enough that the benefits may be marginal.