The NVIDIA H100 PCIe, with its 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, is exceptionally well-suited for running the Llama 3 8B model. Llama 3 8B in its INT8 quantized form requires approximately 8GB of VRAM, leaving a substantial 72GB of headroom. This ample VRAM allows for large batch sizes and concurrent inference tasks. The H100's 14592 CUDA cores and 456 Tensor Cores further accelerate the matrix multiplications and other computations inherent in transformer-based language models like Llama 3, leading to significantly improved inference speeds.
Beyond VRAM capacity, the H100's high memory bandwidth is critical for efficiently transferring model weights and intermediate activations between the GPU's compute units and memory. This prevents bottlenecks and ensures that the CUDA and Tensor cores are fully utilized. The Hopper architecture's advancements, such as the Transformer Engine, are specifically designed to optimize performance for large language models, further boosting throughput and reducing latency compared to previous generation GPUs. The estimated tokens/second rate of 93 reflects the H100's ability to rapidly process and generate text with the Llama 3 8B model.
Given the substantial VRAM headroom, experiment with larger batch sizes to maximize throughput, especially when serving multiple concurrent requests. While INT8 quantization provides a good balance between performance and accuracy, consider experimenting with FP16 or BF16 precision if higher accuracy is desired, keeping in mind the increased VRAM usage. Utilize inference frameworks optimized for NVIDIA GPUs and transformer models, such as vLLM or TensorRT, to further enhance performance. Monitor GPU utilization and memory usage to identify any potential bottlenecks and fine-tune the configuration accordingly.
For optimal performance, ensure that the NVIDIA drivers are up-to-date and that the system has sufficient CPU cores and RAM to support data preprocessing and other auxiliary tasks. Profile the application to identify any CPU-bound operations that might limit overall throughput. Consider using techniques like speculative decoding to potentially increase tokens/second.