The NVIDIA H100 SXM, with its substantial 80GB of HBM3 memory and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Llama 3 8B model. The model, when quantized to INT8, requires only 8GB of VRAM, leaving a significant 72GB of headroom. This ample VRAM allows for large batch sizes and the potential to run multiple instances of the model concurrently. The H100's 16896 CUDA cores and 528 Tensor Cores further accelerate the model's computations, resulting in high throughput and low latency. The Hopper architecture's advanced features, such as the Transformer Engine, are specifically designed to optimize the performance of large language models like Llama 3.
Given the H100's capabilities, prioritize maximizing throughput by experimenting with larger batch sizes. Start with a batch size of 32 and incrementally increase it until you observe diminishing returns or memory constraints. Consider using a high-performance inference framework like vLLM or NVIDIA's TensorRT to further optimize performance. While INT8 quantization is a good starting point, explore FP16 or BF16 for potentially higher accuracy, provided the VRAM headroom remains sufficient. Regularly monitor GPU utilization and memory consumption to fine-tune settings for optimal performance.