The NVIDIA H100 SXM, with its massive 80GB of HBM3 memory and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 2B language model. Gemma 2 2B, even in its full FP16 precision, only requires 4GB of VRAM. When quantized to q3_k_m, the VRAM footprint shrinks dramatically to just 0.8GB. This leaves an enormous 79.2GB of VRAM headroom, ensuring no memory bottlenecks. The H100's 16896 CUDA cores and 528 Tensor Cores further contribute to efficient computation, allowing for high throughput during inference. The Hopper architecture's advanced features like Tensor Core acceleration are fully leveraged by this model.
Given the substantial VRAM headroom and powerful hardware, users should experiment with larger batch sizes (up to 32) to maximize throughput. While q3_k_m quantization provides excellent memory efficiency, consider experimenting with higher precision quantization schemes like q4_k_m or even FP16 if the performance benefits outweigh the increased memory usage. This setup is ideal for serving multiple concurrent requests or running larger, more complex models alongside Gemma 2 2B. Monitor GPU utilization and adjust batch sizes to optimize for latency or throughput based on your specific needs.