The NVIDIA H100 SXM, with its 80GB of HBM3 memory and 3.35 TB/s memory bandwidth, offers substantial resources for running large language models like Llama 3 70B. The analysis indicates excellent compatibility due to the model's quantized VRAM footprint (28GB) being significantly smaller than the H100's available VRAM. The q3_k_m quantization brings the model size down considerably, allowing it to fit comfortably within the GPU's memory. This headroom is crucial for handling larger batch sizes and longer context lengths without running into out-of-memory errors. Furthermore, the H100's Hopper architecture, with its dedicated Tensor Cores, is optimized for accelerating the matrix multiplications that are fundamental to LLM inference.
Given the ample VRAM headroom, users should experiment with increasing the batch size to maximize throughput. While the estimated batch size is 3, the H100 could likely handle a larger batch size, potentially up to 8 or even higher, depending on the context length and specific workload. Employing techniques like speculative decoding, if supported by the inference framework, could further enhance performance. It's also advisable to monitor GPU utilization to ensure the model is fully leveraging the available resources. Consider optimizing the inference kernel to further reduce latency.