The NVIDIA H100 PCIe, with its 80GB of HBM2e memory and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Mini 3.8B model. Phi-3 Mini, in FP16 precision, requires approximately 7.6GB of VRAM, leaving a substantial 72.4GB of headroom on the H100. This ample VRAM allows for large batch sizes and extended context lengths without encountering memory constraints. The H100's 14592 CUDA cores and 456 Tensor Cores further accelerate the model's computations, leading to high throughput and low latency during inference. The Hopper architecture is optimized for transformer models like Phi-3 Mini, ensuring efficient utilization of the GPU's resources.
Given the H100's capabilities, users should aim for high batch sizes (e.g., 32 or higher) to maximize throughput. Experiment with different context lengths up to the model's limit of 128000 tokens to determine the optimal balance between performance and information retention. Consider using inference frameworks optimized for NVIDIA GPUs, such as vLLM or NVIDIA's TensorRT, to further enhance performance. While FP16 precision is sufficient, exploring mixed precision (e.g., bfloat16) might yield additional speedups without significant loss in accuracy.