The NVIDIA H100 SXM, with its massive 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 2B language model, especially in its Q4_K_M (4-bit quantized) form. The quantized model requires a mere 1GB of VRAM, leaving a substantial 79GB of headroom. This abundant VRAM allows for very 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 rapid inference.
Given the relatively small size of the Gemma 2 2B model and the H100's capabilities, the primary performance bottleneck is likely to be software optimization rather than hardware limitations. The high memory bandwidth of the H100 ensures that data can be transferred to and from the GPU cores efficiently, minimizing latency during inference. Expect excellent throughput and low latency, making this setup ideal for real-time applications. The estimated tokens/sec of 135 is a good starting point, but can likely be optimized further with careful tuning.
For optimal performance with Gemma 2 2B on the H100, start with the recommended settings below and experiment with different batch sizes to maximize throughput without sacrificing latency. Consider using a framework like `vLLM` or `text-generation-inference` designed for efficient LLM serving, as they often incorporate optimizations such as continuous batching and optimized kernel implementations. Also, monitor GPU utilization; if it's low, increase the batch size or run multiple model instances concurrently to fully utilize the H100's resources.
While the Q4_K_M quantization provides excellent memory savings, you may want to experiment with higher precision quantization levels (e.g., Q8_0) if accuracy is paramount and you still have ample VRAM headroom. Be sure to benchmark each configuration to determine the optimal balance between performance and accuracy for your specific use case.