The NVIDIA H100 PCIe, with its 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Mixtral 8x7B (46.70B) model, especially when quantized to INT8. Mixtral 8x7B, a sparse mixture-of-experts model, requires significant memory capacity. In FP16, it demands 93.4GB of VRAM, which exceeds the H100's capacity. However, quantizing the model to INT8 reduces the VRAM footprint to 46.7GB. This allows the entire model to fit comfortably within the H100's 80GB VRAM, leaving a substantial 33.3GB headroom for context, batch processing, and other operational overhead.
Furthermore, the H100's architecture, based on Hopper, features 14592 CUDA cores and 456 Tensor Cores, which are crucial for accelerating both inference and training workloads. The high memory bandwidth of 2.0 TB/s ensures that data can be transferred rapidly between the GPU and memory, minimizing bottlenecks. This is particularly important for large language models like Mixtral, where efficient data handling directly impacts performance. The estimated tokens/sec of 54 and a batch size of 3 indicate a balance between throughput and latency, which can be further optimized with appropriate software configurations.
The H100's 350W TDP is also a consideration, requiring adequate cooling and power supply to maintain optimal performance. However, the performance gains achieved by utilizing the H100's advanced architecture and ample VRAM outweigh the power consumption considerations, making it an ideal platform for deploying and running Mixtral 8x7B.
Given the H100's capabilities, focus on optimizing inference speed and memory utilization. Start with the suggested INT8 quantization for optimal VRAM usage. Experiment with different batch sizes to find the sweet spot between throughput and latency for your specific application. Consider using a framework like vLLM or NVIDIA's TensorRT to further optimize inference performance. These frameworks leverage the H100's Tensor Cores and other architectural features to accelerate computations.
Monitor GPU utilization and memory usage during operation. If you encounter performance bottlenecks, investigate further quantization options (e.g., INT4 or even lower bit precisions), but be mindful of potential accuracy trade-offs. Employ techniques like context window optimization to reduce the memory footprint of each inference request. For even higher throughput, consider model parallelism across multiple H100 GPUs, if your application supports it.