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 14B model. The model, requiring 28GB of VRAM in FP16 precision, leaves a substantial 52GB of VRAM headroom on the H100. This ample headroom allows for larger batch sizes, longer context lengths, and potentially running multiple model instances concurrently. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, provides the necessary computational power for efficient inference, ensuring low latency and high throughput. The high memory bandwidth is crucial for rapidly transferring model weights and activations, further enhancing performance.
Given the significant VRAM headroom, experiment with increasing the batch size to maximize throughput. Start with the estimated batch size of 18 and incrementally increase it until you observe diminishing returns or run into memory constraints. Also, explore using a context length close to the model's maximum of 131072 tokens to leverage the full capabilities of Qwen 2.5. Consider using quantization techniques (e.g., int8 or even lower precision) to further improve performance and reduce memory footprint, although the H100's ample VRAM may make this unnecessary for a single model instance. For optimal performance, utilize inference frameworks like vLLM or NVIDIA's TensorRT, which are designed to leverage the H100's hardware acceleration features.