The NVIDIA H100 PCIe, while a powerful GPU, falls short of the VRAM requirements for running Mistral Large 2 in FP16 precision. Mistral Large 2, with its 123 billion parameters, necessitates 246GB of VRAM when using FP16 (half-precision floating point). The H100 PCIe offers only 80GB of HBM2e memory. This substantial deficit of 166GB means the entire model cannot be loaded onto the GPU simultaneously for inference. Consequently, without significant optimization techniques, direct inference is impossible. Memory bandwidth, though a robust 2.0 TB/s on the H100, becomes irrelevant when the primary constraint is insufficient memory capacity. The large context length of 128,000 tokens further exacerbates memory pressure during inference.
To run Mistral Large 2, you'll need to explore several optimization strategies. Quantization is crucial; consider using 4-bit or 8-bit quantization (e.g., QLoRA, bitsandbytes) to drastically reduce the model's memory footprint. Even with quantization, offloading layers to system RAM (CPU) might be necessary, which will significantly slow down inference speed. Alternatively, explore distributed inference across multiple GPUs, if available. If these options are not viable, consider using a smaller model or a cloud-based inference service with sufficient GPU resources. Cloud services often offer optimized environments and managed infrastructure for large language model inference.