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 27B model, especially when quantized. The Q4_K_M quantization reduces the model's memory footprint to a mere 13.5GB, leaving a significant 66.5GB of VRAM headroom. This ample VRAM allows for large batch sizes and extended context lengths without encountering memory constraints. Furthermore, the H100's 14592 CUDA cores and 456 Tensor Cores provide significant computational power, enabling rapid inference.
Given the H100's capabilities, users should leverage the available VRAM by experimenting with larger batch sizes to maximize throughput. Employing a framework like `llama.cpp` or `vLLM` will allow efficient utilization of the hardware and the GGUF quantization. While Q4_K_M offers a good balance between size and accuracy, consider experimenting with slightly higher bit quantization like Q5_K_M if accuracy is paramount and performance remains acceptable. Monitor GPU utilization and temperature to ensure optimal operation, adjusting batch size as needed to maintain consistent performance.