The NVIDIA H100 SXM, with its 80GB of HBM3 memory, provides sufficient VRAM to comfortably run Llama 3 70B when quantized to INT8. Quantization reduces the model's memory footprint, bringing it down to approximately 70GB. This leaves a 10GB headroom, which is beneficial for accommodating the operating system, other processes, and potential memory fragmentation during inference. The H100's impressive 3.35 TB/s memory bandwidth ensures that data can be rapidly transferred between the GPU and memory, minimizing latency and maximizing throughput.
Furthermore, the H100's architecture, featuring 16896 CUDA cores and 528 Tensor Cores, is well-suited for the computational demands of large language models like Llama 3. The Tensor Cores are specifically designed to accelerate matrix multiplications, a core operation in deep learning, which significantly boosts inference speed. While the estimated 63 tokens/sec is a good starting point, actual performance can vary depending on the specific inference framework, batch size, and context length used. Optimizations such as kernel fusion and efficient memory management can further improve the throughput.
Given the H100's capabilities, users should prioritize using optimized inference frameworks like vLLM or NVIDIA's TensorRT to maximize performance. Experiment with different batch sizes, starting with 1, to find the optimal balance between latency and throughput. While INT8 quantization provides a good balance between performance and accuracy, consider experimenting with lower precision formats like FP16 or BF16 if your application requires higher accuracy, keeping in mind the potential increase in VRAM usage. Profile your application to identify bottlenecks and optimize accordingly.
Monitor GPU utilization and memory usage during inference to ensure that the system is operating efficiently. If you encounter performance issues, consider reducing the context length or further optimizing the model using techniques like pruning or distillation. For production deployments, explore techniques like model parallelism or pipeline parallelism to distribute the workload across multiple GPUs if necessary.