The primary bottleneck in running Mixtral 8x22B (141B) on an NVIDIA A100 40GB GPU is the insufficient VRAM. Mixtral 8x22B, even in its INT8 quantized form, requires 141GB of VRAM to load the entire model. The A100 40GB only provides 40GB, leaving a deficit of 101GB. This means the model cannot be fully loaded onto the GPU for inference. While the A100's impressive memory bandwidth of 1.56 TB/s and substantial compute power (6912 CUDA cores, 432 Tensor Cores) are beneficial, they are irrelevant if the model cannot fit into the available memory. The Ampere architecture is well-suited for AI workloads, but memory capacity remains the limiting factor in this scenario.
Attempting to run the model directly will result in an out-of-memory error. Techniques like offloading layers to system RAM could be explored, but this will drastically reduce performance, potentially making inference impractically slow. The high memory bandwidth of the A100 would help mitigate some of the performance impact of offloading, but the sheer size difference between required and available VRAM makes this solution less than ideal. Without significant model parallelism across multiple GPUs, achieving reasonable inference speeds with this configuration is highly unlikely.
Given the significant VRAM discrepancy, running Mixtral 8x22B on a single A100 40GB is not feasible without extreme performance compromises. Consider using a larger GPU with sufficient VRAM (e.g., an A100 80GB or H100) or explore model parallelism across multiple GPUs to distribute the model's memory footprint. Another option is to use a smaller model that fits within the 40GB VRAM.
If using the A100 40GB is a must, investigate extreme quantization techniques beyond INT8, such as 4-bit quantization (QLORA or similar). However, be aware that aggressive quantization can impact model accuracy. Alternatively, explore cloud-based inference services that offer larger GPUs or distributed inference capabilities. These services may provide a more cost-effective solution than purchasing additional hardware.