The NVIDIA H100 PCIe, with its substantial 80GB of HBM2e VRAM and 2.0 TB/s memory bandwidth, offers ample resources for running the Llama 3 8B model. Llama 3 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 concurrent model deployments. The H100's Hopper architecture, featuring 14592 CUDA cores and 456 Tensor Cores, is well-suited for accelerating the matrix multiplications and other computations that are fundamental to LLM inference.
The high memory bandwidth of the H100 is crucial for efficiently transferring model weights and intermediate activations between the GPU's compute units and memory, preventing bottlenecks that can limit performance. This is especially important for models like Llama 3 8B, which involve complex operations across a large number of parameters. The combination of abundant VRAM and high memory bandwidth ensures that the H100 can fully utilize its compute capabilities when running Llama 3 8B.
Given the specifications, we anticipate excellent performance. The estimated 93 tokens/sec and a batch size of 32 are reasonable starting points, but these can be further optimized through careful selection of inference frameworks and quantization techniques. The H100's tensor cores will be instrumental in accelerating FP16 or lower-precision computations, leading to substantial speedups compared to running the model on CPUs or GPUs lacking such specialized hardware.
For optimal performance with Llama 3 8B on the NVIDIA H100 PCIe, begin with an inference framework like vLLM or NVIDIA's TensorRT. These frameworks are designed to maximize GPU utilization and minimize latency. Experiment with different batch sizes to find the sweet spot between throughput and latency. Start with a batch size of 32 and adjust based on observed performance.
Consider quantizing the model to INT8 or even INT4 to further reduce memory footprint and increase inference speed. This can be achieved using libraries like bitsandbytes or AutoGPTQ. Monitor GPU utilization and memory usage to identify potential bottlenecks and adjust settings accordingly. Explore techniques like speculative decoding to potentially increase the tokens/sec rate.