The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM and Ampere architecture, is exceptionally well-suited for running the Phi-3 Mini 3.8B model, especially when quantized to INT8. The model requires only 3.8GB of VRAM in its INT8 quantized form, leaving a substantial 20.2GB of VRAM headroom. This ample VRAM allows for larger batch sizes and longer context lengths, leading to improved throughput and efficiency during inference. The RTX 3090's high memory bandwidth of 0.94 TB/s further ensures that data can be transferred quickly between the GPU and memory, minimizing bottlenecks and maximizing the utilization of the Tensor Cores.
Given the comfortable VRAM headroom, users should experiment with increasing the batch size to maximize GPU utilization and throughput. Utilizing a framework like `vLLM` or `text-generation-inference` can provide significant performance gains through optimized memory management and kernel implementations. While INT8 quantization offers excellent performance with minimal accuracy loss, consider experimenting with FP16 for tasks where higher precision is critical, but be mindful of the increased VRAM usage. Monitor GPU utilization and memory usage to fine-tune settings for optimal performance.