The NVIDIA H100 SXM, with its massive 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, is exceptionally well-suited for running the Llama 3.1 8B model. The Q4_K_M quantization brings the model's VRAM footprint down to a mere 4GB, leaving a substantial 76GB of VRAM headroom. This abundant VRAM allows for large batch sizes and extended context lengths without encountering memory limitations. The H100's Hopper architecture, featuring 16896 CUDA cores and 528 Tensor Cores, provides ample computational resources for accelerating inference. The high memory bandwidth ensures that data can be transferred efficiently between the GPU and memory, minimizing bottlenecks during the forward pass.
Given the model size and the GPU's capabilities, the estimated 108 tokens/sec throughput is a reasonable expectation. This estimation considers the overhead of memory transfers and kernel execution. The H100's Tensor Cores are specifically designed to accelerate matrix multiplications, which are a core component of transformer models like Llama 3.1 8B. The combination of high memory bandwidth, abundant VRAM, and specialized hardware acceleration makes the H100 an ideal platform for running this model efficiently. Furthermore, the large VRAM headroom allows for experimentation with larger batch sizes and context lengths to optimize performance for specific use cases.
With the H100's substantial resources, users should explore optimizing inference parameters to maximize throughput. Start with a batch size of 32 and gradually increase it while monitoring VRAM usage to avoid exceeding the GPU's capacity. Experiment with different context lengths, up to the model's maximum of 128000 tokens, to see how it affects performance. If you observe performance degradation at larger batch sizes or context lengths, consider using techniques like attention quantization or speculative decoding to further improve efficiency. Additionally, leverage libraries like NVIDIA's TensorRT for further optimization of the model.
If you encounter issues despite the ample resources, ensure that you're using the latest NVIDIA drivers and CUDA toolkit. Also, verify that the inference framework (e.g., `llama.cpp`, vLLM) is properly configured to utilize the H100's Tensor Cores. For production deployments, consider using a dedicated inference server like NVIDIA Triton Inference Server to manage resources and handle concurrent requests efficiently. Monitor GPU utilization and power consumption to ensure that the system is operating within its thermal limits.