The NVIDIA RTX 3090 Ti, with its 24GB of GDDR6X VRAM and Ampere architecture, provides ample resources for running the Phi-3 Mini 3.8B model. The model, when quantized to Q4_K_M (4-bit), requires approximately 1.9GB of VRAM. This leaves a substantial 22.1GB headroom, ensuring the model and its associated processes can operate comfortably without exceeding the GPU's memory capacity. The RTX 3090 Ti's 1.01 TB/s memory bandwidth further contributes to efficient data transfer, minimizing potential bottlenecks during inference. The presence of 10752 CUDA cores and 336 Tensor cores accelerates the matrix multiplications and other computations inherent in transformer-based language models like Phi-3, leading to improved inference speeds.
Given the RTX 3090 Ti's capabilities and the model's relatively small footprint, users should prioritize maximizing throughput by experimenting with larger batch sizes. Start with the estimated batch size of 29 and gradually increase it until VRAM utilization approaches its limit or performance plateaus. Employing techniques like speculative decoding or continuous batching can further enhance performance. Ensure your system has adequate cooling to handle the RTX 3090 Ti's 450W TDP, especially during extended inference sessions. For optimal performance, consider using NVIDIA's TensorRT for model optimization and deployment.