The NVIDIA H100 SXM, with its substantial 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 27B model, particularly in its Q4_K_M (4-bit) quantized form. Quantization significantly reduces the model's memory footprint, bringing it down to a mere 13.5GB. This leaves a generous 66.5GB of VRAM headroom on the H100, ensuring ample space for larger batch sizes, longer context lengths, and other concurrent workloads without encountering memory constraints. The H100's 16896 CUDA cores and 528 Tensor Cores will also provide significant computational power for accelerating inference, contributing to high throughput.
The H100's Hopper architecture is optimized for transformer-based models like Gemma 2. The high memory bandwidth is crucial for rapidly transferring model weights and activations during inference, minimizing latency and maximizing throughput. With the model fitting comfortably within VRAM, the primary performance bottleneck will likely be computational throughput, which the H100 is well-equipped to handle. The large VRAM capacity enables the use of larger batch sizes which can further improve throughput by amortizing the overhead of kernel launches and memory transfers.
Given the H100's capabilities and the model's relatively small quantized size, focus on maximizing throughput through batch size optimization. Start with a batch size of 12 as suggested, and experiment with increasing it until you observe diminishing returns or encounter memory limitations. Also, explore different inference frameworks like `vLLM` or NVIDIA's `text-generation-inference` which are designed for high-throughput serving and optimized for NVIDIA GPUs. Ensure you have the latest NVIDIA drivers installed to take full advantage of the H100's hardware capabilities. Profile the inference process using tools like NVIDIA Nsight Systems to identify any potential bottlenecks and fine-tune performance further.