The primary limiting factor for running large language models like Mixtral 8x7B (46.70B) is VRAM. This model, in FP16 precision, requires approximately 93.4GB of VRAM to load the model weights and manage activations during inference. The NVIDIA A100 80GB, while a powerful GPU, only offers 80GB of VRAM. This 13.4GB shortfall prevents the model from being loaded directly into the GPU's memory, resulting in a compatibility failure.
While the A100's impressive 2.0 TB/s memory bandwidth and abundant CUDA and Tensor cores could theoretically provide excellent inference speeds, they are rendered irrelevant when the model cannot fit into VRAM. The Ampere architecture itself is well-suited for transformer-based models, but the physical memory constraint is insurmountable without employing specific techniques to reduce the VRAM footprint. Without optimizations, the model will either fail to load, or it will rely heavily on system RAM, leading to drastically reduced performance due to the slower transfer speeds between system RAM and the GPU.
Directly running Mixtral 8x7B (46.70B) in FP16 on a single A100 80GB is not feasible. To make this model runnable, consider aggressive quantization techniques such as Q4 or even lower. Frameworks like `llama.cpp` or `text-generation-inference` excel at low-bit quantization and CPU offloading. Additionally, look into model parallelism across multiple GPUs if available, or explore techniques like CPU offloading layers, which will significantly reduce performance but allow the model to run.
Alternatively, consider using a smaller model that fits within the A100's VRAM, or utilize cloud-based inference services that offer larger GPU instances. Fine-tuning a smaller, more efficient model for your specific task may also be a worthwhile approach, providing a balance between performance and resource utilization.