The NVIDIA H100 SXM, with its 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, offers ample resources for running the Gemma 2 27B model. Gemma 2 27B in FP16 precision requires approximately 54GB of VRAM, leaving a substantial 26GB headroom on the H100. This headroom is beneficial for accommodating larger batch sizes, longer context lengths, or other memory-intensive operations during inference. The H100's 528 Tensor Cores are also crucial for accelerating the matrix multiplications that are fundamental to transformer-based language models like Gemma 2, enabling efficient and fast inference.
Given the H100's capabilities, you can expect excellent performance with Gemma 2 27B. Start with a batch size of 4 and a context length of 8192 tokens. Monitor VRAM usage and adjust these parameters to maximize throughput without exceeding the available memory. Consider using a high-performance inference framework like vLLM or NVIDIA's TensorRT to further optimize performance. If you need to reduce VRAM usage, experiment with quantization techniques like INT8, but be aware that this might slightly impact the model's accuracy.