The NVIDIA H100 SXM, with its substantial 80GB of HBM3 memory and 3.35 TB/s memory bandwidth, offers ample resources for running the Llama 3.1 8B model. Llama 3.1 8B, requiring approximately 16GB of VRAM in FP16 precision, fits comfortably within the H100's memory capacity, leaving a significant 64GB headroom for larger batch sizes, longer context lengths, or even running multiple model instances concurrently. The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor Cores, is highly optimized for transformer-based models like Llama 3.1 8B, ensuring efficient matrix multiplications and accelerated inference.
The high memory bandwidth of the H100 is crucial for minimizing latency during model inference, particularly with the extended context length of 128000 tokens. This allows for rapid data transfer between the GPU's memory and compute units, preventing bottlenecks and maximizing throughput. The estimated tokens/sec of 108 and a batch size of 32 are indicative of the H100's ability to handle the model's computational demands with ease, providing a responsive and performant experience. The large VRAM headroom also allows for experimentation with higher precision (e.g., BF16) or fine-tuning the model directly on the H100.
Given the H100's capabilities, users should leverage inference frameworks like vLLM or NVIDIA's TensorRT to optimize performance further. Experiment with different batch sizes to find the optimal balance between throughput and latency for your specific application. Utilizing techniques such as quantization (e.g., to INT8 or even INT4, if supported without significant accuracy loss) can further improve inference speed and reduce memory footprint, potentially allowing for even larger batch sizes or concurrent model instances. Consider using distributed inference techniques if scaling beyond a single H100 is desired.
Monitor GPU utilization and memory usage during inference to identify potential bottlenecks. Adjust batch size and context length accordingly. Ensure that your data pipeline is optimized to feed data to the GPU efficiently. Profile your application to identify any performance bottlenecks outside of the model inference itself. Regular updates to NVIDIA drivers and the chosen inference framework are recommended to benefit from the latest performance improvements and bug fixes.