The NVIDIA RTX 4080 SUPER is an excellent choice for running the BGE-Large-EN embedding model. With 16GB of GDDR6X VRAM and a memory bandwidth of 0.74 TB/s, the 4080 SUPER significantly exceeds the model's modest 0.7GB VRAM requirement in FP16 precision. This large VRAM headroom allows for substantial batch sizes, improving throughput and overall inference efficiency. The Ada Lovelace architecture, with its 10240 CUDA cores and 320 Tensor cores, provides ample computational resources for the matrix multiplications and other operations inherent in transformer-based models like BGE-Large-EN.
BGE-Large-EN's relatively small size (0.33B parameters) means that the 4080 SUPER's computational power is more than sufficient, leading to high token generation rates. The high memory bandwidth ensures that data can be transferred quickly between the GPU and memory, minimizing bottlenecks. The combination of ample VRAM, high memory bandwidth, and powerful compute capabilities results in a smooth and efficient inference experience. Users can expect to achieve high throughput and low latency when using this GPU with the BGE-Large-EN model, especially when optimizing for batch size and quantization.
Given the substantial VRAM headroom, experiment with increasing the batch size to maximize GPU utilization and throughput. Start with a batch size of 32 and gradually increase it until you observe diminishing returns in terms of tokens per second. Consider using a high-performance inference framework such as vLLM or text-generation-inference to further optimize performance. These frameworks often incorporate techniques like continuous batching and optimized kernel implementations.
Although FP16 precision is sufficient for BGE-Large-EN, investigate quantization techniques like INT8 or even lower precision formats if you need to further reduce memory footprint or increase inference speed. However, be mindful of potential accuracy trade-offs when using lower precision formats. Monitor the model's output quality to ensure that quantization does not significantly degrade performance for your specific use case. Finally, ensure that your system has adequate cooling to handle the 4080 SUPER's 320W TDP, especially when running computationally intensive tasks for extended periods.