The NVIDIA H100 SXM, with its substantial 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Qwen 2.5 7B model. The model, when quantized to INT8, requires only 7GB of VRAM, leaving a significant 73GB of headroom. This ample VRAM allows for large batch sizes and extended context lengths, crucial for maximizing throughput and handling complex, long-form text generation tasks. The H100's 16896 CUDA cores and 528 Tensor Cores further accelerate the model's computations, contributing to its high performance. The Hopper architecture is optimized for transformer models like Qwen, fully leveraging the GPU's capabilities.
Given the substantial VRAM headroom, experiment with larger batch sizes (up to 32 or even higher, depending on your specific application) to increase throughput. Leverage frameworks like vLLM or NVIDIA's TensorRT to further optimize inference speed. Consider using techniques like speculative decoding to potentially push the tokens/sec even higher. Monitor GPU utilization to ensure the H100 is being fully utilized; if utilization is low, increase the batch size or context length. While INT8 quantization is efficient, explore FP16 or BF16 for potentially improved accuracy if the application demands it, keeping in mind the increased VRAM usage.