The primary limiting factor for running Mixtral 8x7B (46.7B) on an NVIDIA H100 PCIe is the available VRAM. Mixtral 8x7B in FP16 precision requires approximately 93.4GB of VRAM to load the model weights and perform inference. The NVIDIA H100 PCIe provides 80GB of VRAM, resulting in a shortfall of 13.4GB. This means that the model, in its default FP16 configuration, cannot be directly loaded onto the GPU. While the H100's impressive 2.0 TB/s memory bandwidth and powerful Tensor Cores would otherwise contribute to fast inference, the insufficient VRAM prevents the model from even being initialized.
Even if the model could be loaded, the large context length of 32768 tokens will further increase VRAM usage during inference. Without sufficient VRAM, the system will likely encounter out-of-memory errors. Techniques like offloading layers to system RAM could be employed, but this would drastically reduce performance due to the much slower data transfer rates between system RAM and the GPU. The H100's architecture is designed for high-throughput, low-latency operations within its VRAM, and leveraging system RAM negates these advantages.
Due to the VRAM limitation, directly running Mixtral 8x7B in FP16 on the NVIDIA H100 PCIe is not feasible. To make it work, consider using quantization techniques such as 8-bit integer (INT8) or 4-bit integer (INT4) quantization. Quantization reduces the memory footprint of the model, potentially bringing it within the H100's VRAM capacity. However, be aware that quantization can impact model accuracy, so evaluate the trade-off between memory usage and performance for your specific application.
Alternatively, explore techniques like model parallelism, where the model is split across multiple GPUs. If you have access to multiple H100 GPUs, this approach could enable you to run the model without significant quantization. Also, consider using inference frameworks optimized for large language models, such as vLLM or FasterTransformer, as they often incorporate memory-efficient techniques and optimized kernels that can improve performance even with limited VRAM.