The NVIDIA H100 PCIe, with its 80GB of HBM2e memory and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Qwen 2.5 7B model. Qwen 2.5 7B, requiring approximately 14GB of VRAM in FP16 precision, leaves a substantial 66GB of headroom on the H100. This ample VRAM allows for large batch sizes and extended context lengths, maximizing GPU utilization and throughput. 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.
The high memory bandwidth of the H100 is crucial for efficiently transferring model weights and intermediate activations between the GPU's compute units and memory. This minimizes memory bottlenecks and ensures that the Tensor Cores are kept fully occupied, leading to optimal performance. Furthermore, the H100's power envelope of 350W allows for sustained high performance without thermal throttling, a critical factor for long-running inference tasks.
The estimated tokens/second rate of 117 is a reasonable expectation given the model size and GPU capabilities. However, the actual performance can vary depending on the specific inference framework used, the input prompt complexity, and the level of optimization applied.
For optimal performance, leverage an optimized inference framework such as vLLM or NVIDIA's TensorRT. These frameworks can significantly reduce latency and increase throughput compared to naive implementations. Experiment with different batch sizes to find the sweet spot between latency and throughput. A batch size of 32 is a good starting point, but you may be able to increase it further depending on your specific application. Consider using techniques like quantization (e.g., INT8 or FP16) to further reduce memory footprint and potentially improve performance, although FP16 is already a good balance for this setup.
Monitor GPU utilization and memory usage to identify any bottlenecks. If you encounter memory limitations, consider reducing the batch size or using a more aggressive quantization scheme. If you are primarily concerned with minimizing latency, prioritize optimizing the inference kernel and reducing the overhead associated with data transfer between the CPU and GPU.