The NVIDIA H100 SXM, with its massive 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Llama 3 8B model. The model, when quantized to Q4_K_M (4-bit), requires only 4GB of VRAM, leaving a substantial 76GB 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 will ensure rapid computation, critical for achieving low latency and high throughput during inference. The Hopper architecture is specifically designed for transformer models, making it an ideal match for Llama 3.
Given the significant VRAM headroom, experiment with larger batch sizes to maximize throughput. Start with a batch size of 32, as indicated, and incrementally increase it until you observe diminishing returns in terms of tokens/sec or experience memory-related errors. Also, explore the use of techniques like speculative decoding to further enhance inference speed. Consider using a higher precision (e.g., FP16) if you require improved accuracy and have the VRAM available. For production deployments, monitor GPU utilization and power consumption to optimize resource allocation and minimize costs.