The NVIDIA H100 SXM, with its substantial 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, provides ample resources for running the Phi-3 Medium 14B model. Phi-3 Medium, requiring 28GB of VRAM in FP16 precision, fits comfortably within the H100's memory capacity, leaving a significant 52GB headroom for larger batch sizes, longer context lengths, or concurrent model deployments. The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor cores, is well-suited for the computational demands of large language models, enabling efficient matrix multiplications and other tensor operations crucial for inference.
The high memory bandwidth of the H100 ensures rapid data transfer between the GPU and its memory, minimizing bottlenecks during model execution. This is particularly important for large models like Phi-3 Medium, where frequent memory access is required. The combination of abundant VRAM and high memory bandwidth allows the H100 to process large batches of data concurrently, leading to increased throughput and reduced latency. Furthermore, the H100's Tensor Cores are specifically designed to accelerate deep learning workloads, providing significant performance gains compared to traditional CUDA cores.
To maximize performance, leverage the H100's Tensor Cores by utilizing FP16 or BF16 precision. Experiment with different batch sizes to find the optimal balance between throughput and latency. Consider using inference frameworks like vLLM or NVIDIA's TensorRT to further optimize performance. Monitor GPU utilization and memory consumption to identify potential bottlenecks and adjust settings accordingly. If you encounter memory limitations despite the available headroom, investigate memory fragmentation or inefficient data handling within your inference pipeline.
For optimal throughput, explore techniques like speculative decoding or continuous batching. These methods can increase the utilization of the H100's computational resources. Regularly profile your inference workload to identify performance bottlenecks and adjust your configuration accordingly. Remember to keep your NVIDIA drivers and CUDA toolkit up to date to benefit from the latest performance optimizations.