The Mixtral 8x22B model, with its 141 billion parameters, poses a significant challenge for even high-end GPUs due to its substantial memory footprint. When using FP16 (half-precision floating point), the model requires approximately 282GB of VRAM to load and operate. The NVIDIA H100 PCIe, while a powerful accelerator, is equipped with 80GB of HBM2e memory. This discrepancy of 202GB means the model, in its native FP16 format, cannot fit entirely within the GPU's memory. Consequently, direct inference without optimization is impossible.
Furthermore, even if techniques like offloading were employed, performance would be severely hampered. While the H100's 2.0 TB/s memory bandwidth is impressive, constantly swapping model layers between system RAM and GPU memory would introduce unacceptable latency. The Hopper architecture's Tensor Cores would be underutilized due to the bottleneck created by the memory limitations. The 14592 CUDA cores would also be waiting for data, leading to suboptimal parallel processing.
Essentially, running Mixtral 8x22B on a single H100 PCIe without significant optimization is infeasible. The model's size simply exceeds the GPU's memory capacity, resulting in either a failure to load or extremely poor performance due to constant data transfer between the GPU and system memory.
To run Mixtral 8x22B, you'll need to drastically reduce its memory footprint. Quantization is essential. Consider using 4-bit quantization (bitsandbytes or similar) to reduce the model size by a factor of 4, potentially bringing the memory requirements down to a manageable level. Even with quantization, you might need to explore techniques like model parallelism across multiple GPUs or CPU offloading for certain layers, accepting a performance hit.
Alternatively, consider using a smaller model that fits within the H100's VRAM. Many excellent language models with fewer parameters offer a good balance between performance and resource requirements. If you absolutely need Mixtral 8x22B, investigate cloud-based solutions that offer instances with multiple high-VRAM GPUs or explore distributed inference frameworks designed for large models.