The NVIDIA H100 SXM, with its 80GB of HBM3 memory and 3.35 TB/s memory bandwidth, offers substantial resources for running large language models. The Gemma 2 2B model, requiring only 4GB of VRAM in FP16 precision, fits comfortably within the H100's memory capacity, leaving a significant headroom of 76GB for larger batch sizes or concurrent model deployments. The H100's 16896 CUDA cores and 528 Tensor Cores further accelerate the model's computations, resulting in high throughput during inference. The Hopper architecture's advanced features, such as the Transformer Engine, are specifically designed to optimize the performance of transformer-based models like Gemma 2.
Given the ample VRAM and computational power of the H100, the primary performance bottleneck is likely to be memory bandwidth. While 3.35 TB/s is substantial, optimizing data transfer between the GPU and system memory is crucial. Techniques like kernel fusion and optimized data layouts can further enhance performance. The estimated 135 tokens/sec is a solid starting point, but real-world performance will vary depending on the specific workload, input sequence length, and inference framework used. The large VRAM also allows for experimentation with larger batch sizes, potentially increasing throughput at the expense of latency.
For optimal performance with Gemma 2 2B on the H100, start with a batch size of 32 and experiment with larger values to maximize throughput. Consider using an optimized inference framework like vLLM or NVIDIA's TensorRT to further accelerate inference. Quantization to INT8 or even lower precision may provide additional speedups with minimal impact on accuracy. Monitor GPU utilization and memory consumption to identify potential bottlenecks and adjust settings accordingly. If encountering memory issues, reduce the batch size or switch to a lower precision format.
Experiment with different context lengths to find the optimal balance between performance and accuracy. While the model supports 8192 tokens, shorter context lengths may result in faster inference times. Use profiling tools to identify performance bottlenecks and optimize accordingly. Consider using techniques like speculative decoding to further improve throughput.