The NVIDIA H100 SXM, with its 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, offers substantial resources for running large language models like Gemma 2 9B. Gemma 2 9B, requiring approximately 18GB of VRAM in FP16 precision, fits comfortably within the H100's memory capacity, leaving a significant 62GB headroom for larger batch sizes, longer context lengths, or concurrent model execution. The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor Cores, is specifically designed for accelerating deep learning workloads, ensuring efficient matrix multiplications and other compute-intensive operations crucial for LLM inference.
Furthermore, the H100's high memory bandwidth allows for rapid data transfer between the GPU and memory, minimizing bottlenecks and maximizing throughput. This is particularly important for maintaining a high tokens/second rate during inference. The estimated 108 tokens/second rate reflects the powerful combination of ample VRAM, high memory bandwidth, and specialized Tensor Cores optimized for the Transformer architecture prevalent in Gemma 2 9B. The large VRAM headroom also allows experimentation with larger batch sizes, potentially further increasing throughput at the cost of increased latency.
Given the substantial VRAM headroom, experiment with larger batch sizes to maximize throughput, keeping an eye on latency. Consider using a high-performance inference framework like vLLM or NVIDIA's TensorRT to further optimize performance. While FP16 offers a good balance of speed and accuracy, explore quantization techniques such as INT8 or even INT4 to potentially increase throughput even further, though this may come with a small trade-off in accuracy. Monitor GPU utilization and memory usage to fine-tune batch size and context length for optimal performance.