The "8x22B" refers to Mixtral's Mixture of Experts (MoE) architecture. For you, this primarily means two things: capability and efficiency.
It's not a single 176B parameter model. It's 8 separate "expert" neural networks (each 22B parameters), with a router network that selects the 2 most relevant experts for any given input token. This allows it to approach the performance of a much larger model while requiring the active computational cost of roughly a 44B parameter model during inference. You get broader, more capable responses without a proportional increase in latency or cost. In practice, this translates to better performance on complex, multi-step tasks (like code generation or detailed analysis) compared to a dense model of similar serving cost.