The primary limiting factor for running large language models (LLMs) like Mixtral 8x22B is VRAM. Mixtral 8x22B in FP16 precision requires approximately 282GB of VRAM to load the model weights and perform inference. The NVIDIA RTX 4090, while a powerful GPU, is equipped with only 24GB of VRAM. This creates a significant shortfall of 258GB, rendering the model incompatible for direct loading and inference in FP16. Even with techniques like offloading layers to system RAM, the performance would be severely bottlenecked by the relatively slow transfer speeds between the GPU and system memory. Memory bandwidth, while substantial on the RTX 4090 (1.01 TB/s), becomes less relevant when the entire model cannot reside on the GPU.
Given the VRAM limitations, direct inference with Mixtral 8x22B on a single RTX 4090 is not feasible without substantial compromises. Consider using quantization techniques like 4-bit or even lower precision (e.g., using `bitsandbytes` or `llama.cpp`) to significantly reduce the VRAM footprint. Alternatively, explore distributed inference solutions that split the model across multiple GPUs or machines. Cloud-based inference services provide another viable option, abstracting away the hardware requirements and offering optimized performance for demanding models like Mixtral 8x22B.