The NVIDIA RTX 3090, while a powerful GPU with 24GB of GDDR6X VRAM, falls short of the VRAM requirements for running Mixtral 8x22B (141.00B) even with aggressive quantization. Mixtral 8x22B, a large language model with 141 billion parameters, demands substantial memory. Even when quantized to Q4_K_M (4-bit), it still requires approximately 70.5GB of VRAM. The RTX 3090 has a memory bandwidth of 0.94 TB/s, which is sufficient for transferring data to the GPU, but the limiting factor is the available VRAM itself. The deficit of 46.5GB means the model cannot be fully loaded onto the GPU, preventing successful inference. The 10496 CUDA cores and 328 Tensor cores are effectively bottlenecked by the VRAM limitation, rendering their computational power unusable for this specific model.
Due to the significant VRAM shortfall, running Mixtral 8x22B (141.00B) on a single RTX 3090 is not feasible. Consider using a multi-GPU setup with NVLink to pool VRAM, or explore cloud-based solutions offering GPUs with sufficient memory. Alternatively, smaller models with fewer parameters, such as those in the 7B to 13B range, might be more suitable for the RTX 3090. If using a smaller model is not an option, offloading layers to system RAM (CPU) using llama.cpp might allow you to run the model, but performance will be severely impacted. Consider using cloud services or renting a GPU with sufficient memory if performance is critical.