The NVIDIA H100 PCIe, with its substantial 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 2B model. Gemma 2 2B, even in its full FP16 precision, requires only 4GB of VRAM, leaving a significant 76GB of headroom. By leveraging INT8 quantization, the VRAM footprint is further reduced to a mere 2GB, maximizing the available resources for larger batch sizes and longer context lengths. The H100's 14592 CUDA cores and 456 Tensor Cores provide ample computational power to accelerate the matrix multiplications and other operations crucial for LLM inference. The Hopper architecture's advancements in tensor core utilization and memory management further enhance the model's performance.
Given the H100's high memory bandwidth, the model's inference speed will primarily be determined by computational throughput rather than memory limitations. This allows for exploring larger batch sizes to improve overall efficiency without sacrificing latency. The estimated tokens/sec of 117 is a strong starting point, but can likely be improved with careful selection of inference frameworks and optimization techniques. Utilizing the full potential of the H100's Tensor Cores through optimized kernels will be key to achieving optimal performance. Furthermore, the large VRAM capacity allows for experimentation with multiple model instances or larger context windows without encountering memory constraints.
For optimal performance, leverage an inference framework like vLLM or NVIDIA's TensorRT, specifically designed to exploit the capabilities of NVIDIA GPUs. Start with a batch size of 32 and experiment with increasing it until you observe diminishing returns or increased latency. Explore techniques like speculative decoding and attention optimization to further enhance throughput. Consider profiling the application to identify any bottlenecks and fine-tune the configuration accordingly. While INT8 quantization is a good starting point, experiment with FP16 or BF16 if higher precision is required, as the H100 has ample VRAM and compute to handle these formats efficiently.
If you encounter performance issues, verify that the NVIDIA drivers are up-to-date and that the GPU is operating at its rated clock speeds. Monitor GPU utilization to ensure that the model is fully leveraging the available resources. If the GPU is underutilized, consider increasing the batch size or exploring more aggressive optimization techniques. For deployments requiring very low latency, consider using a smaller batch size and prioritizing single-request processing.