The NVIDIA A100 40GB, equipped with 40GB of HBM2e memory and a memory bandwidth of 1.56 TB/s, falls short of the VRAM requirements for running Llama 3.1 70B (70.00B) in INT8 quantization. While INT8 reduces the VRAM footprint compared to FP16, it still demands approximately 70GB of VRAM. The A100's 40GB VRAM results in a deficit of 30GB, preventing the model from loading entirely onto the GPU. This limitation stems from the model's substantial 70 billion parameters, necessitating a large memory capacity to store the model's weights and intermediate activations during inference. The Ampere architecture provides excellent computational capabilities with its 6912 CUDA cores and 432 Tensor cores, but these cannot compensate for the lack of sufficient memory.
Directly running Llama 3.1 70B (70.00B) on a single A100 40GB is not feasible due to VRAM limitations. Consider using model parallelism across multiple A100 GPUs if available, which would split the model across several GPUs, alleviating the VRAM bottleneck on each individual card. Alternatively, explore more aggressive quantization techniques such as INT4 or even GPTQ, which can significantly reduce the VRAM footprint, potentially bringing it within the A100's capacity. However, be aware that aggressive quantization can impact the model's accuracy. Another option is offloading some layers to system RAM, but this will dramatically decrease inference speed. Finally, consider using a smaller model variant if performance is critical and the task allows.