The NVIDIA H100 SXM, with its substantial 80GB of HBM3 VRAM and a memory bandwidth of 3.35 TB/s, is exceptionally well-suited for running the Qwen 2.5 14B language model, especially when quantized to INT8. The model requires only 14GB of VRAM in INT8, leaving a significant 66GB of headroom. This ample VRAM allows for larger batch sizes and longer context lengths without encountering memory constraints. Furthermore, the H100's 16896 CUDA cores and 528 Tensor Cores will significantly accelerate the model's computations, ensuring fast inference speeds.
The H100's Hopper architecture is designed to maximize performance in AI workloads. The high memory bandwidth is critical for quickly transferring model weights and intermediate activations, preventing bottlenecks during inference. The Tensor Cores, optimized for mixed-precision calculations, will further enhance the speed of INT8 operations. Given the considerable VRAM headroom, users can experiment with larger batch sizes to increase throughput, optimizing the utilization of the H100's processing power. The estimated 90 tokens/sec is a reasonable expectation, but actual performance will depend on the specific inference framework and settings used.
However, it is important to monitor GPU utilization and temperature. While the H100 has sufficient resources, running at high utilization for extended periods can lead to thermal throttling. Efficient code and proper cooling are essential to maintain consistent performance.
For optimal performance, leverage an inference framework like vLLM or NVIDIA's TensorRT, as these are designed to maximize throughput on NVIDIA GPUs. Start with a batch size around 23, as estimated, but experiment to find the sweet spot that balances latency and throughput for your specific application. Given the available VRAM, consider increasing the context length to fully utilize Qwen 2.5's capabilities. Profile your application with different batch sizes and context lengths to understand the performance trade-offs.
While INT8 quantization offers excellent VRAM savings and speed improvements, consider experimenting with FP16 or BF16 if higher precision is required and performance remains acceptable. If you encounter performance issues, ensure that you are using the latest NVIDIA drivers and CUDA toolkit. Monitor GPU utilization and temperature to identify potential bottlenecks and ensure proper cooling.