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 (141.00B) model, especially when quantized. The q3_k_m quantization brings the model's VRAM footprint down to a manageable 56.4GB. This leaves a comfortable 23.6GB VRAM headroom, ensuring smooth operation and preventing out-of-memory errors during inference. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor cores, provides significant computational power for accelerating the model's matrix multiplications and other operations.
Memory bandwidth is crucial for LLM performance, as it dictates how quickly data can be transferred between the GPU's memory and its processing units. The H100's 2.0 TB/s bandwidth is more than adequate for the Mixtral 8x22B model, even with its large parameter size. This high bandwidth allows for rapid loading of model weights and intermediate activations, minimizing latency and maximizing throughput. The estimated 31 tokens/sec, though an estimate, reflects the balance between the model's complexity, the GPU's capabilities, and the chosen quantization level. This performance is suitable for interactive applications and research purposes.
For optimal performance with the Mixtral 8x22B model on the NVIDIA H100 PCIe, prioritize using an efficient inference framework like `llama.cpp` or `vLLM`. `llama.cpp` is known for its CPU and GPU offloading capabilities, allowing you to fine-tune the balance between VRAM usage and inference speed. `vLLM` is designed for high-throughput inference and can efficiently manage the model's memory footprint. Experiment with different quantization levels (e.g., q4_k_m) to potentially improve performance further, but be mindful of the trade-off between quantization and accuracy.
Start with a batch size of 1, as suggested, and gradually increase it to find the optimal balance between throughput and latency. Monitor GPU utilization and memory usage to identify any bottlenecks. Consider using techniques like speculative decoding or attention mechanism optimization to further enhance performance. Ensure that your system has adequate cooling to prevent thermal throttling, as the H100 has a TDP of 350W.