The NVIDIA H100 SXM, with its 80GB of HBM3 VRAM and 3.35 TB/s memory bandwidth, is a powerhouse for AI workloads. However, running Llama 3.1 70B (70.00B) in FP16 precision presents a significant challenge. Llama 3.1 70B (70.00B) requires approximately 140GB of VRAM for the model weights alone when using FP16. This greatly exceeds the H100's 80GB capacity, resulting in a VRAM deficit of 60GB. Without sufficient VRAM, the model cannot be fully loaded onto the GPU, preventing successful inference.
Due to the substantial VRAM requirement of Llama 3.1 70B (70.00B) in FP16, direct inference on a single H100 SXM is not feasible. To run this model, consider employing quantization techniques such as 4-bit or 8-bit quantization. This can significantly reduce the VRAM footprint, potentially bringing it within the H100's capacity. Alternatively, explore distributed inference across multiple GPUs, where the model is partitioned and loaded across several devices. Cloud platforms often provide such multi-GPU setups.