The NVIDIA H100 SXM, with its 80GB of HBM3 memory and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 2B model. With INT8 quantization, the model requires only 2GB of VRAM, leaving a substantial 78GB of headroom. This abundant VRAM allows for large batch sizes and the potential to run multiple instances of the model concurrently. The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor Cores, provides ample computational power for accelerating inference.
The memory bandwidth of the H100 is critical for efficiently loading model weights and processing data. The 3.35 TB/s bandwidth ensures that data can be moved between the GPU's memory and compute units quickly, minimizing bottlenecks. This is particularly important for large language models, where memory bandwidth can often be a limiting factor. The Tensor Cores in the H100 are specifically designed to accelerate matrix multiplications, which are a fundamental operation in neural networks, further enhancing performance.
Given the ample resources, the estimated tokens/sec of 135 and a batch size of 32 are conservative estimates. Real-world performance may exceed these figures depending on the specific implementation and optimization techniques employed. The H100's raw compute power and memory capacity make it an ideal platform for deploying and scaling the Gemma 2 2B model.
Given the H100's capabilities, focus on maximizing throughput by experimenting with larger batch sizes and optimizing the inference pipeline. Consider using a high-performance inference framework like vLLM or NVIDIA's TensorRT to further accelerate inference. Profiling the application is crucial to identify any bottlenecks and optimize accordingly. Monitor GPU utilization and memory consumption to ensure efficient resource allocation.
While INT8 quantization is a good starting point, explore other quantization techniques, such as FP16 or even FP8 (if supported by your chosen framework), to potentially improve performance further. Remember to balance quantization levels with accuracy, as aggressive quantization can sometimes lead to a slight degradation in output quality. Experiment with different context lengths to find the optimal trade-off between memory usage and the model's ability to handle long sequences.