The NVIDIA H100 SXM, with its 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, offers substantial resources for running large language models. Llama 3.1 8B, in its INT8 quantized form, requires only 8GB of VRAM, leaving a significant 72GB headroom. This ample VRAM allows for large batch sizes and the ability to handle extended context lengths without encountering memory constraints. The H100's 16896 CUDA cores and 528 Tensor Cores further contribute to accelerating the model's computations, ensuring low latency and high throughput during inference.
The H100's Hopper architecture is designed for efficient matrix multiplication, a core operation in deep learning. The high memory bandwidth ensures that data can be transferred between the GPU and memory quickly, minimizing bottlenecks. With INT8 quantization, the model's memory footprint is reduced, enabling faster data transfer and improved computational efficiency. This combination of factors results in a high tokens/second generation rate, making the H100 an excellent choice for serving Llama 3.1 8B in real-time applications.
The estimated tokens/second rate of 108 is a reflection of the H100's processing power and memory bandwidth capabilities when running Llama 3.1 8B in INT8. The large VRAM headroom allows for a batch size of 32, which can significantly increase throughput and reduce latency, especially in multi-user scenarios. However, it's important to note that these numbers can vary depending on the specific inference framework used, the prompt complexity, and other system-level factors.
To maximize performance, utilize an optimized inference framework such as vLLM or NVIDIA's TensorRT. These frameworks leverage GPU acceleration and memory management techniques to improve throughput and reduce latency. Experiment with different batch sizes to find the optimal balance between throughput and latency for your specific use case. Start with a batch size of 32 and adjust as needed.
Consider using techniques like speculative decoding or continuous batching to further enhance performance. Monitor GPU utilization and memory usage to ensure that the H100 is being fully utilized. Regularly update your drivers and inference framework to take advantage of the latest performance improvements and bug fixes. If you are running into issues related to context length, explore techniques like attention mechanisms or sparse attention to reduce memory usage.