The NVIDIA H100 PCIe, with its 80GB of HBM2e memory and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 2B model. Gemma 2 2B, even in its unquantized FP16 format, requires only 4GB of VRAM, leaving a substantial 76GB of headroom. By using the q3_k_m quantization, the model's memory footprint is reduced to a mere 0.8GB, further optimizing resource utilization. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, provides ample computational power for accelerating inference. The high memory bandwidth ensures rapid data transfer between the GPU and memory, preventing bottlenecks during model execution.
Given the H100's capabilities, the primary constraint on performance shifts from hardware limitations to software optimization. The estimated tokens/sec of 117 and batch size of 32 represent a baseline expectation. With optimized inference frameworks and potentially higher quantization levels (if acceptable for the specific use case), these figures can be significantly improved. The H100's Tensor Cores are specifically designed for accelerating matrix multiplications, which are fundamental operations in deep learning models like Gemma 2 2B. This allows for faster computation and higher throughput.
For optimal performance, utilize an inference framework like `vLLM` or NVIDIA's `TensorRT` which are optimized for NVIDIA GPUs and can leverage the H100's Tensor Cores. Start with a batch size of 32 and experiment with increasing it to maximize GPU utilization, monitoring for latency increases. While q3_k_m quantization provides a small memory footprint, consider experimenting with higher precision quantization levels (e.g., q4_k_m or even FP16 if memory allows) to potentially improve output quality, as the H100 has ample VRAM to accommodate larger models or less aggressive quantization.
Also, ensure the NVIDIA drivers are up-to-date to take advantage of the latest performance improvements and bug fixes. Profile the inference process to identify any potential bottlenecks, such as data loading or pre/post-processing steps. If using a multi-GPU setup, explore model parallelism to further distribute the workload and increase throughput.