The NVIDIA H100 PCIe, boasting 80GB of HBM2e VRAM and a 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Gemma 2 2B language model. Gemma 2 2B, with its relatively small 2 billion parameters, requires only 4GB of VRAM when using FP16 precision. The H100's massive VRAM headroom (76GB) ensures ample space for larger batch sizes, extended context lengths, and even multiple model instances simultaneously. Furthermore, the H100's Hopper architecture, including its 14592 CUDA cores and 456 Tensor Cores, provides significant computational power for accelerating the matrix multiplications and other operations inherent in transformer-based language models like Gemma 2.
The H100's high memory bandwidth is crucial for efficiently transferring data between the GPU's compute units and memory, preventing bottlenecks that can limit performance. With 2.0 TB/s bandwidth, the H100 can easily handle the memory access patterns of Gemma 2, even under heavy load. The estimated 117 tokens/sec inference speed reflects this efficient utilization of resources. This speed can be further optimized through techniques like quantization and optimized inference frameworks.
Given the H100's capabilities, focus on maximizing throughput and minimizing latency. Start with a batch size of 32 and experiment with larger values to find the optimal balance between throughput and latency for your specific application. Explore different inference frameworks like vLLM or NVIDIA's TensorRT to further accelerate performance. Quantization to INT8 or even lower precisions could potentially improve performance with minimal impact on accuracy, but thoroughly evaluate the impact on your specific use case.
Consider using techniques like speculative decoding or continuous batching to further boost performance. Monitor GPU utilization to ensure that the H100 is being fully utilized. If you're only using a small fraction of the GPU's resources, consider running multiple instances of the model or deploying larger models to take full advantage of the available hardware.