The NVIDIA H100 SXM, with its substantial 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, offers ample resources for running the Gemma 2 9B model, especially when quantized. Gemma 2 9B, in its full FP16 precision, requires approximately 18GB of VRAM. However, utilizing q3_k_m quantization significantly reduces this footprint to a mere 3.6GB. This leaves a considerable VRAM headroom of 76.4GB, allowing for large batch sizes and potentially the concurrent deployment of multiple model instances or other AI workloads on the same GPU.
Furthermore, the H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor Cores, is exceptionally well-suited for the computational demands of large language models. The high memory bandwidth ensures rapid data transfer between the GPU's processing units and memory, minimizing bottlenecks during inference. The combination of abundant VRAM, high memory bandwidth, and powerful compute capabilities makes the H100 an ideal platform for deploying Gemma 2 9B and similar models.
Based on the provided data, the estimated tokens/sec throughput is 108, and the suggested batch size is 32. These figures are estimates and can vary based on specific implementation details, such as the inference framework used and the level of optimization applied. However, they provide a good baseline expectation for performance.
Given the substantial VRAM headroom, consider experimenting with larger batch sizes to maximize GPU utilization and throughput. While q3_k_m quantization provides excellent memory savings, evaluate the impact on model accuracy. If accuracy is critical, explore higher precision quantization levels (e.g., q4_k_m or even FP16 if VRAM allows) and compare the performance trade-offs. Monitor GPU utilization and temperature to ensure optimal operation, especially when pushing the limits of batch size or running multiple model instances.
For deployment, leverage optimized inference frameworks like `vLLM` or `text-generation-inference`. These frameworks offer techniques like continuous batching and optimized kernel implementations to further enhance throughput and reduce latency. Ensure you have the latest NVIDIA drivers installed to take full advantage of the H100's capabilities.