The NVIDIA H100 SXM, with its 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Llama 3.1 8B model. The model, when quantized to q3_k_m, requires only 3.2GB of VRAM, leaving a substantial 76.8GB headroom. This ample VRAM allows for large batch sizes and extended context lengths, significantly boosting throughput. The H100's 16896 CUDA cores and 528 Tensor Cores further accelerate the matrix multiplications and other computations central to LLM inference, resulting in high tokens/second generation.
Given the H100's architecture and memory bandwidth, the primary performance bottleneck will likely be the computational intensity of the model itself rather than memory constraints. The Hopper architecture's optimized Tensor Cores are specifically designed to handle the mixed-precision computations common in quantized LLMs like Llama 3.1 8B. The estimated 108 tokens/sec is a good starting point, but can likely be further optimized. The large VRAM allows for experimentation with larger batch sizes to maximize GPU utilization.
For optimal performance, leverage the H100's capabilities by maximizing the batch size without exceeding the available VRAM or significantly increasing latency. Start with a batch size of 32, as indicated, and experiment with increasing it further. Utilize a framework like `vLLM` or `text-generation-inference` to take advantage of features such as continuous batching and optimized kernel implementations. These frameworks can significantly improve throughput and reduce latency compared to more basic inference setups.
Consider profiling the inference process to identify any potential bottlenecks. While VRAM isn't a concern, CPU utilization and data transfer rates between the CPU and GPU could become limiting factors. Experiment with different data loading and preprocessing techniques to minimize CPU overhead. If performance is still not satisfactory, explore further quantization options (e.g., Q2 or even Q4) to reduce the model's memory footprint and computational requirements, though this may come at the cost of some accuracy.