The NVIDIA H100 SXM, with its substantial 80GB of HBM3 memory, is exceptionally well-suited for running large language models like Gemma 2 27B. The model's 27 billion parameters, when quantized to INT8, require approximately 27GB of VRAM. This leaves a significant 53GB of headroom on the H100, allowing for larger batch sizes, longer context lengths, and potentially the concurrent deployment of other models or tasks. The H100's impressive 3.35 TB/s memory bandwidth ensures that data can be rapidly transferred between the GPU and memory, minimizing bottlenecks during inference.
Furthermore, the H100's Hopper architecture, with its 16896 CUDA cores and 528 Tensor Cores, is optimized for AI workloads. The Tensor Cores are specifically designed to accelerate matrix multiplications, which are fundamental to deep learning operations. This, combined with the high memory bandwidth, contributes to the expected performance of around 90 tokens per second. The INT8 quantization further enhances performance by reducing memory footprint and computational demands without significant loss of accuracy.
Given the ample VRAM headroom, users can experiment with larger batch sizes to maximize throughput. Starting with a batch size of 9 is a good baseline, but increasing it further, while monitoring GPU utilization and latency, can lead to better performance. Consider using a high-performance inference framework like vLLM or NVIDIA's TensorRT to further optimize the model execution. These frameworks often incorporate techniques such as kernel fusion and graph optimization to minimize overhead and maximize GPU utilization.
While INT8 quantization provides a good balance between performance and accuracy, users can also explore FP16 or BF16 precision if higher accuracy is required and VRAM usage remains within acceptable limits. However, be mindful of the potential performance trade-off. Also, ensure that the context length is set appropriately for the specific use case, as longer context lengths can increase VRAM usage and latency.