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. The model, when quantized to q3_k_m, requires only 5.6GB of VRAM, leaving a significant 74.4GB of headroom. This ample VRAM allows for large batch sizes and extended context lengths without encountering memory limitations. The H100's 14592 CUDA cores and 456 Tensor Cores provide substantial computational power for both inference and potential fine-tuning tasks.
Given the memory bandwidth and compute capabilities of the H100, the Phi-3 Medium 14B model can leverage the hardware effectively. The estimated tokens/second generation rate of 78 is a strong indicator of responsive performance. The large VRAM headroom means that users can experiment with larger batch sizes (estimated at 26) to maximize throughput, especially when serving multiple concurrent requests. The Hopper architecture's optimizations for transformer models further enhance the efficiency of the inference process.
For optimal performance, leverage an inference framework like vLLM or NVIDIA's TensorRT. These frameworks are designed to maximize throughput and minimize latency on NVIDIA GPUs. Experiment with different batch sizes to find the sweet spot between latency and throughput for your specific use case. Monitor GPU utilization to ensure the H100 is being fully utilized; if not, consider increasing the batch size or number of concurrent requests.
While q3_k_m provides excellent memory savings, consider experimenting with higher precision quantization (e.g., q4_k_m or even FP16 if memory allows) to potentially improve the model's accuracy, especially for tasks requiring high precision. However, be mindful of the increased VRAM usage and adjust batch sizes accordingly.