The NVIDIA H100 SXM, with its 80GB of HBM3 memory, is well-suited for running the Mixtral 8x22B (141.00B) model, especially when using quantization. The q3_k_m quantization brings the model's VRAM footprint down to 56.4GB, leaving a comfortable 23.6GB of headroom. This headroom is crucial for accommodating the overhead of the operating system, inference framework, and intermediate activations during computation. The H100's impressive 3.35 TB/s memory bandwidth ensures that data can be efficiently transferred between the GPU and memory, minimizing bottlenecks during inference.
Furthermore, the H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor Cores, is optimized for deep learning workloads. The Tensor Cores are particularly beneficial for accelerating matrix multiplications, which are fundamental operations in transformer-based models like Mixtral. While the model's parameter size is substantial, the combination of high VRAM, memory bandwidth, and specialized hardware allows for efficient processing. Expect a throughput of approximately 36 tokens per second, which is a good starting point for interactive applications, although higher throughputs are possible with further optimization.
To maximize performance, leverage an optimized inference framework such as vLLM or NVIDIA's TensorRT. These frameworks can further optimize memory usage and computation graphs, leading to higher throughput and lower latency. Experiment with different batch sizes; while a batch size of 1 is a safe starting point, increasing it (if VRAM allows) can improve overall throughput. Monitor GPU utilization and memory usage to identify any potential bottlenecks.
Consider using techniques like speculative decoding or continuous batching if the application requires even lower latency. Ensure the NVIDIA drivers are up to date to take advantage of the latest performance improvements and bug fixes. Profile the application to identify specific areas that could benefit from further optimization.