The NVIDIA RTX 4070 Ti SUPER, equipped with 16GB of GDDR6X VRAM and an Ada Lovelace architecture, offers ample resources for running the BGE-Large-EN embedding model. BGE-Large-EN, with its 0.33B parameters, requires approximately 0.7GB of VRAM when using FP16 precision. This leaves a substantial 15.3GB of VRAM headroom, allowing for larger batch sizes and the potential to run multiple instances of the model concurrently or alongside other applications without encountering memory constraints. The 4070 Ti SUPER's memory bandwidth of 0.67 TB/s further ensures efficient data transfer between the GPU and memory, minimizing potential bottlenecks during inference.
Given the 4070 Ti SUPER's 8448 CUDA cores and 264 Tensor cores, the model should achieve excellent performance. The Ada Lovelace architecture is optimized for AI workloads, leveraging Tensor Cores to accelerate matrix multiplications, a core operation in neural networks. The estimated tokens/second throughput of 90 indicates a responsive and efficient inference speed. This makes the combination suitable for real-time applications where low latency is crucial. The large VRAM capacity also allows for experimentation with larger context lengths, potentially improving the quality of the embeddings generated.
For optimal performance, start with a batch size of 32 and a context length of 512 tokens, as these are known working parameters. Monitor GPU utilization and VRAM usage to fine-tune these settings further. Consider using a framework like `text-generation-inference` for optimized serving, which can provide significant performance improvements compared to naive implementations. Experiment with different precisions (e.g., FP16 vs. INT8) to balance performance and accuracy. If you need to run multiple models or larger batch sizes simultaneously, carefully monitor VRAM usage to avoid out-of-memory errors.