The NVIDIA H100 PCIe, with its 80GB of HBM2e memory and 2.0 TB/s memory bandwidth, provides ample resources for running the Gemma 2 27B model, especially when quantized. The q3_k_m quantization reduces the model's VRAM footprint to a mere 10.8GB, leaving a substantial 69.2GB of VRAM headroom. This generous headroom allows for larger batch sizes and longer context lengths without encountering memory constraints. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, is well-suited for the matrix multiplications and other computations inherent in large language model inference. The high memory bandwidth ensures that data can be efficiently transferred between the GPU's compute units and memory, minimizing bottlenecks.
Given the H100's computational power and memory bandwidth, the estimated tokens/sec of 78 is a reasonable expectation. The specific tokens/sec will vary based on factors such as prompt complexity, batch size, and the specific inference framework used. The large VRAM headroom also enables experimentation with larger batch sizes. Increasing the batch size can improve throughput by processing more requests concurrently, but it also increases latency. Finding the optimal batch size is crucial for balancing throughput and latency requirements. The H100's TDP of 350W should also be considered, ensuring adequate cooling and power supply are available in the system.
For optimal performance, leverage an inference framework optimized for NVIDIA GPUs like vLLM or NVIDIA's TensorRT. Experiment with different batch sizes to find the sweet spot between throughput and latency. Start with a batch size of 12 and gradually increase it until you observe diminishing returns or memory limitations. Monitor GPU utilization and memory usage during inference to identify potential bottlenecks. Consider using techniques like speculative decoding to further improve the tokens/sec if supported by your chosen framework. Remember to profile your application to identify any other potential performance bottlenecks outside of the GPU itself.
While q3_k_m quantization provides significant memory savings, it may slightly impact model accuracy. If accuracy is paramount and you have sufficient VRAM headroom, consider experimenting with higher precision quantization levels like q4_k_m or even FP16, but be aware of increased memory usage and potential performance impacts. Be sure to validate the impact of quantization on your specific use case to ensure acceptable results.