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 9B model. Gemma 2 9B, even in its unquantized FP16 (16-bit floating point) form, requires only 18GB of VRAM. By leveraging INT8 quantization, the VRAM footprint is further reduced to a mere 9GB. This leaves a significant VRAM headroom of 71GB, enabling the simultaneous deployment of multiple model instances or the handling of very large batch sizes and context lengths without encountering memory limitations.
The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, ensures rapid tensor operations, which are fundamental to LLM inference. The high memory bandwidth facilitates quick data transfer between the GPU and memory, minimizing latency and maximizing throughput. Given these hardware capabilities and the model's relatively small size after quantization, the H100 can achieve impressive inference speeds. The estimated tokens/second rate of 93 is a testament to this, and the large VRAM headroom allows for aggressive batching to further boost performance.
Furthermore, the H100's Tensor Cores are specifically designed to accelerate matrix multiplications, which are at the heart of deep learning computations. By utilizing these cores efficiently, the H100 can deliver significantly higher performance compared to GPUs without dedicated tensor processing units. The H100's power consumption of 350W is also a factor to consider, ensuring adequate cooling and power supply are in place for sustained operation.
The NVIDIA H100 PCIe is an excellent choice for running Gemma 2 9B, especially with INT8 quantization. To maximize performance, utilize a high-performance inference framework like vLLM or NVIDIA's TensorRT. Experiment with different batch sizes to find the optimal balance between latency and throughput. Since you have ample VRAM, consider increasing the batch size until you observe diminishing returns in terms of tokens/second.
While INT8 quantization is already effective, you could explore further quantization techniques like GPTQ or AWQ for potentially even smaller model sizes and faster inference. However, be mindful of potential accuracy trade-offs. Monitor GPU utilization and temperature to ensure the H100 is operating within its thermal limits, especially when pushing for maximum throughput. Profile your inference pipeline to identify any bottlenecks and optimize accordingly.