The NVIDIA RTX 3090 Ti, while a powerful GPU, falls short when running the Mixtral 8x7B (46.70B) model due to insufficient VRAM. Mixtral 8x7B in FP16 precision requires approximately 93.4GB of VRAM to load the model weights and activations. The RTX 3090 Ti only offers 24GB of VRAM. This discrepancy of 69.4GB means the model cannot be loaded entirely onto the GPU for inference. The high memory bandwidth of 1.01 TB/s on the 3090 Ti is irrelevant in this case, as the model's size is the limiting factor, not the speed at which data can be transferred.
Because the model exceeds the GPU's VRAM capacity, direct inference is impossible without employing techniques to reduce memory footprint. Without these techniques, the model will either fail to load or experience severe performance degradation due to constant swapping between system RAM and GPU VRAM, resulting in practically unusable inference speeds. The CUDA cores and Tensor cores cannot be effectively utilized if the model isn't resident in the GPU memory.
To run Mixtral 8x7B on an RTX 3090 Ti, you'll need to significantly reduce the model's memory footprint. Quantization is crucial; experiment with 4-bit (Q4) or even lower precision quantization methods. Use an inference framework that supports offloading layers to system RAM or disk, such as llama.cpp with its `n_gpu_layers` parameter, or `text-generation-inference` with tensor parallelism across multiple GPUs (if available) or CPU offloading. Even with these optimizations, expect significantly reduced performance compared to running the model on a GPU with sufficient VRAM.
Consider alternative solutions if performance is critical. This could involve using a cloud-based GPU with more VRAM or distributing the model across multiple GPUs. Alternatively, explore smaller models that fit within the 3090 Ti's VRAM capacity, albeit at the cost of model quality.