The NVIDIA A100 40GB, with its 40GB of HBM2e memory, is a powerful GPU designed for demanding AI workloads. However, running Llama 3 70B in INT8 quantization requires approximately 70GB of VRAM, significantly exceeding the A100's capacity. While the A100's 1.56 TB/s memory bandwidth and Ampere architecture with 6912 CUDA cores and 432 Tensor Cores are substantial, the VRAM limitation prevents the model from loading entirely onto the GPU. This leads to an 'out-of-memory' error, halting inference.
Even with advanced memory management techniques, the 30GB VRAM deficit is too large to overcome without significantly impacting performance. Techniques like offloading layers to system RAM are possible, but this introduces substantial latency due to the slower transfer speeds between GPU and system memory. Consequently, the model would likely be unusable in practice due to extremely slow token generation.
Due to the insufficient VRAM, running Llama 3 70B on a single NVIDIA A100 40GB is not feasible without severe performance degradation. Consider using a GPU with at least 70GB of VRAM, such as an NVIDIA H100 80GB or A100 80GB, or explore multi-GPU setups with appropriate software support for model parallelism. Alternatively, consider using a smaller model like Llama 3 8B, which requires significantly less VRAM and can run efficiently on the A100 40GB. Quantization to lower precisions like INT4 could be explored, but this may affect the model's accuracy and requires careful evaluation.