The NVIDIA H100 SXM, with its 80GB of HBM3 memory, is exceptionally well-suited for running the Mixtral 8x7B model, particularly when quantized. The Q4_K_M quantization reduces the model's VRAM footprint to approximately 23.4GB, leaving a substantial 56.6GB of VRAM headroom. This ample VRAM allows for comfortable operation, larger batch sizes, and extended context lengths without encountering memory limitations. The H100's impressive 3.35 TB/s memory bandwidth ensures rapid data transfer between the GPU and memory, further enhancing performance.
Furthermore, the H100's 16896 CUDA cores and 528 Tensor Cores provide significant computational power, enabling efficient execution of the Mixtral 8x7B model. The Hopper architecture is optimized for transformer-based models like Mixtral, leveraging Tensor Cores to accelerate matrix multiplications, a core operation in deep learning. This combination of high memory capacity, bandwidth, and computational resources results in excellent performance for inference tasks. The estimated 63 tokens/sec is a strong indicator of the H100's capability to handle this model effectively.
Given the significant VRAM headroom, users should experiment with increasing the batch size to maximize throughput. Start with a batch size of 6 and incrementally increase it while monitoring GPU utilization and memory usage. Utilize the llama.cpp framework for optimal performance with GGUF quantized models. Also, consider enabling CUDA graph support within llama.cpp to further reduce latency and improve tokens/second. For production environments, explore using NVIDIA's Triton Inference Server for optimized deployment and management of the Mixtral 8x7B model.
While Q4_K_M offers a good balance of performance and memory usage, explore other quantization methods if higher accuracy is required. However, be mindful of the increased VRAM requirements associated with higher precision quantization. Regularly monitor GPU temperature and power consumption, as the H100 has a TDP of 700W and requires adequate cooling.