The NVIDIA H100 PCIe, with its 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, offers substantial resources for running large language models. The Llama 3 70B model, when quantized to q3_k_m, requires approximately 28GB of VRAM. This leaves a significant 52GB VRAM headroom, indicating that the H100 can comfortably accommodate the model and potentially allow for larger batch sizes or concurrent model deployments. The H100's 14592 CUDA cores and 456 Tensor Cores will be leveraged for the matrix multiplications and other computations inherent in transformer-based inference, contributing to the overall performance.
Memory bandwidth is crucial for feeding data to the compute units. The H100's 2.0 TB/s bandwidth ensures that data can be transferred efficiently between memory and processing units, minimizing bottlenecks. While the provided estimate of 54 tokens/sec is a good starting point, actual performance can vary depending on the specific inference framework, prompt complexity, and other system configurations. The specified batch size of 3 is a reasonable starting point and can be tuned to optimize throughput without exceeding the available memory or negatively impacting latency.
Given the comfortable VRAM headroom, experiment with larger batch sizes to maximize throughput. Start by incrementally increasing the batch size (e.g., from 3 to 4, 5, or 6) and monitor performance using tools like `nvidia-smi` to ensure that you're not running into memory limitations or performance degradation. Also, consider using optimized inference frameworks like vLLM or NVIDIA's TensorRT to further improve performance. Regularly update your NVIDIA drivers to benefit from the latest performance optimizations.
If you encounter performance bottlenecks, profile your code to identify specific areas that are consuming the most resources. Techniques like kernel fusion and mixed-precision arithmetic (if not already enabled by the inference framework) can further optimize performance. Consider using a more aggressive quantization scheme (e.g., Q2 or even Q1) if accuracy degradation is acceptable to further reduce VRAM usage and potentially increase throughput.