The NVIDIA H100 SXM, with its substantial 80GB of HBM3 memory, offers ample VRAM for running the Mixtral 8x7B (46.70B) model, especially when quantized to INT8. The INT8 quantization reduces the model's memory footprint to approximately 46.7GB, leaving a significant 33.3GB VRAM headroom. This headroom is crucial for accommodating larger batch sizes, longer context lengths, and the overhead of the inference framework itself. The H100's impressive 3.35 TB/s memory bandwidth ensures rapid data transfer between the GPU and memory, which is vital for minimizing latency during inference. Furthermore, the Hopper architecture, with its 16896 CUDA cores and 528 Tensor Cores, provides the computational power necessary for efficient matrix multiplications and other operations inherent in transformer models like Mixtral 8x7B.
Given the H100's specifications, the estimated throughput of 63 tokens/sec and a batch size of 3 are reasonable starting points. However, these values can vary depending on the specific implementation and optimization techniques employed. The high memory bandwidth of the H100 is particularly beneficial for handling the model's context length of 32768 tokens, as it allows for fast retrieval and processing of relevant information from the context window. The large VRAM headroom also allows for experimentation with larger batch sizes to potentially improve throughput, although this should be balanced against latency considerations.
For optimal performance, leverage an optimized inference framework such as vLLM or NVIDIA's TensorRT. These frameworks can take full advantage of the H100's Tensor Cores and memory bandwidth. Start with a batch size of 3 and experiment with increasing it until you observe diminishing returns or increased latency. Profile the inference process to identify any bottlenecks, such as data loading or kernel execution, and address them accordingly. Consider using techniques like speculative decoding to further improve token generation speed. Also, regularly update your NVIDIA drivers and CUDA toolkit to benefit from the latest performance enhancements and bug fixes.
While INT8 quantization is a good starting point, consider experimenting with other quantization methods like FP16 or BF16 if your use case allows for slightly reduced accuracy in exchange for increased speed. Monitor GPU utilization and memory usage during inference to ensure that the H100 is being fully utilized. If you encounter out-of-memory errors, reduce the batch size or context length. Regularly monitor the model's outputs to ensure that the quantization process is not introducing unacceptable levels of error.