The NVIDIA H100 PCIe, with its substantial 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Medium 14B model. Phi-3 Medium 14B, requiring 28GB of VRAM in FP16 precision, leaves a significant 52GB of VRAM headroom on the H100. This ample headroom not only ensures smooth operation but also allows for larger batch sizes and longer context lengths, maximizing throughput. The H100's 14592 CUDA cores and 456 Tensor Cores further contribute to efficient computation, accelerating both inference and training tasks.
The high memory bandwidth of the H100 is crucial for feeding the GPU cores with the necessary data, preventing bottlenecks and ensuring optimal utilization of the available compute resources. This is particularly important for large language models like Phi-3 Medium 14B, which are memory-intensive. The combination of abundant VRAM and high memory bandwidth enables the H100 to handle the model's parameters and activations with ease, leading to faster inference times and improved overall performance. The Hopper architecture provides additional optimizations for transformer models, further enhancing the efficiency of the setup.
Given the H100's capabilities, users should aim to maximize batch size and context length to fully utilize the available resources. Experiment with different inference frameworks like vLLM or text-generation-inference to find the best balance between latency and throughput. Quantization to INT8 or even lower precisions could further improve performance without significant loss in accuracy, allowing for even larger batch sizes. However, FP16 should provide excellent performance and quality to start with. Monitor GPU utilization to ensure the H100 is being fully utilized; if not, increase batch size or context length until utilization plateaus.