The NVIDIA H100 PCIe, with its substantial 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 9B model. The model, when quantized to Q4_K_M (4-bit), requires only 4.5GB of VRAM, leaving a significant headroom of 75.5GB. This ample VRAM allows for large batch sizes and the ability to handle longer context lengths without encountering memory limitations. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, will significantly accelerate the matrix multiplication operations crucial for LLM inference, leading to high throughput.
Furthermore, the high memory bandwidth of the H100 ensures that data can be rapidly transferred between the GPU's memory and processing units, preventing bottlenecks during inference. Even with a large batch size, the memory bandwidth is unlikely to be a limiting factor. Given the specifications, we can expect very fast inference speeds. The estimated 93 tokens/sec is a reasonable expectation, and with further optimization, it could potentially be increased. The large VRAM headroom means you could even run multiple instances of the model concurrently or fine-tune it, if desired.
Given the H100's capabilities, prioritize maximizing throughput and minimizing latency. Start with a batch size of 32 as a baseline and experiment with larger values to find the optimal point before performance degrades. Focus on using an inference framework optimized for NVIDIA GPUs, such as vLLM or NVIDIA's TensorRT, to take full advantage of the H100's Tensor Cores. If you are not already using it, make sure to leverage CUDA graphs to reduce CPU overhead and improve overall performance.
For further optimization, explore techniques like speculative decoding and continuous batching to further improve throughput. Monitor GPU utilization and memory usage to identify potential bottlenecks and adjust settings accordingly. Consider profiling the model to identify specific kernels that could benefit from custom optimization. Also, make sure your drivers are up to date to take advantage of the latest optimizations from NVIDIA.