The NVIDIA H100 PCIe, while a powerful GPU, falls short of the VRAM requirements for directly running the Qwen 2.5 72B model in FP16 precision. Qwen 2.5 72B, with its 72 billion parameters, necessitates approximately 144GB of VRAM when using half-precision floating-point (FP16) format. The H100 PCIe offers 80GB of HBM2e memory, resulting in a VRAM deficit of 64GB. This means the entire model cannot reside on the GPU's memory simultaneously, precluding straightforward inference.
Furthermore, even if the model could somehow fit, the memory bandwidth of 2.0 TB/s on the H100 PCIe would become a bottleneck. Loading model weights and intermediate activations during inference would be significantly constrained, leading to severely reduced throughput. Techniques like offloading layers to system RAM could be employed, but this would exacerbate performance issues due to the slower bandwidth of system memory compared to HBM2e. The 14592 CUDA cores and 456 Tensor Cores, while substantial, cannot compensate for the lack of sufficient VRAM and the potential memory bandwidth limitations.
Given the VRAM limitation, directly running Qwen 2.5 72B on a single H100 PCIe in FP16 is not feasible. Several strategies can be employed to mitigate this issue. Quantization is crucial; consider using 4-bit quantization (QLoRA or similar) to drastically reduce the model's memory footprint. This might bring the VRAM usage down to a manageable level. Alternatively, explore model parallelism across multiple GPUs if available. If neither of these options is viable, consider using a smaller model or a cloud-based solution with sufficient GPU memory.
Another option is to offload some of the model layers to the CPU RAM. However, this will significantly reduce the inference speed. It is also important to carefully choose the inference framework. Some frameworks, like vLLM, are optimized for high throughput and low latency, which can help to improve the performance. Finally, consider reducing the context length to reduce the memory footprint.