The NVIDIA H100 PCIe, with its substantial 80GB of HBM2e memory and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Phi-3 Medium 14B model. The model, when quantized to Q4_K_M (4-bit), requires only 7.0GB of VRAM, leaving a significant 73.0GB of headroom. This ample VRAM allows for large batch sizes and extended context lengths without encountering memory limitations. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, provides significant computational power for accelerating the matrix multiplications and other operations inherent in large language model inference.
Given the H100's high memory bandwidth, the model's performance will be primarily bound by compute rather than memory transfer speeds. The estimated 78 tokens/sec suggests efficient utilization of the H100's Tensor Cores. A larger batch size of 26 can be accommodated due to the available VRAM, further improving throughput by amortizing the overhead of kernel launches and memory transfers. The Hopper architecture's optimized memory hierarchy and compute units contribute to achieving this performance level, making it ideal for real-time and high-throughput inference scenarios.
For optimal performance with Phi-3 Medium 14B on the H100, prioritize using inference frameworks that leverage TensorRT or similar optimization techniques to fully utilize the H100's Tensor Cores. Experiment with different batch sizes to find the sweet spot between latency and throughput. While the Q4_K_M quantization provides excellent VRAM savings, consider experimenting with higher precision quantizations (e.g., Q8_0) if latency is not a primary concern, as this could potentially improve output quality. Monitor GPU utilization to ensure that the model is fully leveraging the H100's capabilities.
If you encounter performance bottlenecks, profile your code to identify specific areas for optimization. Check for efficient data loading and pre-processing pipelines. Consider offloading certain operations to the CPU if they are not computationally intensive and are causing GPU stalls. Regularly update your NVIDIA drivers and CUDA toolkit to benefit from the latest performance improvements and bug fixes.