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 model, especially in its quantized form. The Q4_K_M (4-bit) quantization significantly reduces the model's VRAM footprint to approximately 23.4GB, leaving a substantial 56.6GB VRAM headroom. This ample headroom ensures that the H100 can comfortably accommodate the model, along with any additional memory requirements for intermediate calculations during inference, without encountering memory-related bottlenecks.
Furthermore, the H100's 14592 CUDA cores and 456 Tensor Cores provide considerable computational power, which directly translates to faster inference speeds. While the model parameters are substantial at 46.7B, the H100's architecture is designed to handle large models efficiently. The high memory bandwidth is crucial for quickly transferring data between the GPU and memory, maximizing the utilization of the CUDA and Tensor cores. The estimated 54 tokens/sec and batch size of 6 indicate a balance between throughput and latency, suitable for interactive applications and batch processing alike.
Given the H100's capabilities and the model's quantized size, users should prioritize maximizing batch size to improve throughput. Experimenting with larger batch sizes (up to the estimated limit of 6) can significantly increase the number of tokens processed per second. Additionally, leveraging inference frameworks optimized for NVIDIA GPUs, such as TensorRT or vLLM, can further enhance performance. Ensure that the NVIDIA drivers are up-to-date to benefit from the latest optimizations and bug fixes. If experiencing latency issues, consider reducing the context length or using a more aggressive quantization method, although this may impact output quality.
For optimal performance, monitor GPU utilization and memory usage during inference. If the GPU is not fully utilized, it suggests there may be a bottleneck elsewhere, such as CPU preprocessing or data loading. In such cases, optimizing these aspects can lead to further improvements. If the 54 tokens/sec is insufficient, try other quantization types to see if a slightly larger model fits in memory without sacrificing too much speed.