The NVIDIA H100 PCIe, with its 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is well-suited for running the Mixtral 8x22B (141B) model, particularly when employing quantization techniques. The Q4_K_M (4-bit) quantization significantly reduces the model's memory footprint to approximately 70.5GB. This allows the entire model to fit comfortably within the H100's VRAM, leaving a headroom of 9.5GB for operational overhead and intermediate calculations during inference. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, further accelerates the computations involved in processing the model's parameters.
Given the ample VRAM and computational power of the H100, users should prioritize optimizing for throughput. Start with a batch size of 1 and carefully monitor GPU utilization. Experimenting with higher batch sizes might be possible depending on the specific inference framework and workload characteristics. It's crucial to select an efficient inference framework like `llama.cpp` or `vLLM` to maximize performance. If you encounter memory issues, consider offloading some layers to CPU memory, although this will reduce inference speed.