The NVIDIA H100 SXM, with its 80GB of HBM3 memory, demonstrates excellent compatibility with the quantized Mixtral 8x22B model. Mixtral 8x22B, in its Q4_K_M (4-bit GGUF) quantized form, requires approximately 70.5GB of VRAM. The H100's substantial memory bandwidth of 3.35 TB/s ensures rapid data transfer between the GPU and memory, critical for minimizing latency during inference. This headroom allows the H100 to comfortably load the entire model into VRAM, preventing performance bottlenecks associated with swapping data between system RAM and GPU memory.
The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor Cores, is particularly well-suited for the parallel processing demands of large language models. The tensor cores are specifically designed to accelerate matrix multiplication operations, which are fundamental to deep learning inference. Given the model size and quantization level, the estimated throughput is approximately 36 tokens per second. While the batch size is limited to 1, optimizing the context length can further enhance performance within the VRAM constraints.
For optimal performance, leverage the llama.cpp inference framework, which is well-optimized for GGUF models and takes advantage of the H100's architecture. While the current Q4_K_M quantization provides good VRAM utilization, experiment with slightly higher quantization levels (e.g., Q5_K_M or Q6_K) if you encounter any performance limitations, balancing VRAM usage with potential accuracy gains. Monitor GPU utilization and memory usage during inference to identify any potential bottlenecks and fine-tune settings accordingly.
Consider using techniques like speculative decoding if supported by your chosen framework to potentially increase the tokens/second. Also, ensure that your system has sufficient CPU resources to handle the data preprocessing and post-processing stages of the inference pipeline, as this can sometimes become a bottleneck.