Gold standard MoE for performance-per-VRAM on a 48GB system. Battle-tested JSON mode and function calling. The preferred baseline for enterprise RAG pipelines needing 65K context without multi-GPU complexity.
This model requires a specialized High-VRAM environment. Ensure you have the latest CUDA Drivers or Metal Framework installed.
Minimum VRAM: 50GB VRAM Recommended
The Mixtral 8x22B model by Mistral AI is a 141B (MoE) parameter architecture optimized for chat tasks. It requires approximately 48GB of VRAM to comfortably run locally using a Q4_K_M quantization. Extending the context window up to 65,536 tokens will dynamically allocate further VRAM, meaning high-bandwidth memory hardware is strictly advised.
For running Mixtral 8x22B at maximum tokens-per-second, we recommend using LM Studio or Ollama with a GGUF quantization (Q4_K_M or Q6_K). If you are multi-GPU, use vLLM to distribute the layers across your VRAM pool for optimal throughput.
You will need a GPU with at least 50GB of VRAM to run the Q4_K_M quantized version smoothly with a moderate context window.
The simplest method is utilizing Ollama by executing 'ollama run mixtral:8x22b' directly in your command line. Alternatively, you can search for the model via LM Studio's interface.