The NVIDIA H100 PCIe, with its 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 27B model, especially when utilizing INT8 quantization. Gemma 2 27B, in its INT8 quantized form, requires approximately 27GB of VRAM. The H100's ample 80GB VRAM provides a substantial 53GB headroom, ensuring that the model and its associated processes can operate comfortably without memory constraints. This headroom also allows for larger batch sizes and longer context lengths, enhancing overall throughput and enabling more complex and nuanced text generation.
Beyond VRAM, the H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, is optimized for AI workloads. The Tensor Cores significantly accelerate matrix multiplications, which are fundamental to deep learning operations. The high memory bandwidth ensures that data can be transferred to and from the GPU's processing units quickly, preventing bottlenecks and maximizing utilization of the computational resources. With an estimated 78 tokens/sec and a batch size of 9, the H100 provides a responsive and efficient inference experience for Gemma 2 27B. The H100's power consumption of 350W is also a factor to consider, ensuring adequate cooling and power supply are available.
Given the H100's capabilities, users should leverage the INT8 quantized version of Gemma 2 27B for optimal performance and memory utilization. Experiment with increasing the batch size, potentially beyond 9, to further improve throughput, but monitor VRAM usage to avoid exceeding the available capacity. Using inference frameworks optimized for NVIDIA GPUs, such as TensorRT or vLLM, can further boost performance. Also, consider utilizing techniques like speculative decoding to further improve tokens/sec.
If VRAM becomes a constraint due to larger batch sizes or longer context lengths, explore techniques like model parallelism or activation checkpointing, though these may add complexity to the implementation. If encountering unexpected performance issues, profile the application to identify potential bottlenecks, such as data loading or pre/post-processing steps.