The NVIDIA RTX 4090, with its 24GB of GDDR6X VRAM and Ada Lovelace architecture, offers ample resources for running the BGE-M3 embedding model. BGE-M3, at 0.5 billion parameters, requires only 1GB of VRAM when using FP16 precision. This leaves a substantial 23GB of VRAM headroom, allowing for large batch sizes and concurrent execution of other tasks. The RTX 4090's 1.01 TB/s memory bandwidth ensures rapid data transfer between the GPU and memory, further enhancing performance. The 16384 CUDA cores and 512 Tensor Cores will also contribute to accelerating the embedding generation process.
Given the significant VRAM headroom, users can experiment with larger batch sizes to maximize throughput. The Ada Lovelace architecture includes advancements in Tensor Cores that specifically benefit transformer-based models like BGE-M3. This leads to faster matrix multiplications and improved overall efficiency. Expect exceptionally low latency and high throughput when using this combination. The estimated 90 tokens/sec provides a good starting point, but actual performance may vary based on the specific inference framework and optimization techniques employed.
The RTX 4090 is an excellent choice for running BGE-M3. Start with a batch size of 32 and a context length of 8192 tokens. Experiment with increasing the batch size until you observe diminishing returns in throughput or encounter memory limitations. Consider using an optimized inference framework such as ONNX Runtime or TensorRT to further improve performance. For maximum performance, ensure you have the latest NVIDIA drivers installed and that your system has sufficient CPU and RAM to avoid bottlenecks. If you are encountering memory errors, try reducing the batch size or using a lower precision format like INT8.