A new open-source architecture called Moe is making it feasible to run very large language models on standard computer hardware. The system is based on a Mixture-of-Experts (MoE) design, which is key to its efficiency. For instance, the massive gpt-oss-120b model, with 120 billion parameters, activates only a tiny fraction about 5.1 billion for any given task.
This is achieved by routing each query through just one of its 36 layers and four of its 128 specialized expert networks. This sparse activation means the computational load is low enough that a modern CPU can handle the processing, though a GPU remains significantly faster for the task.
The primary role of VRAM shifts from storing the entire model to holding the active context, allowing for longer and more complex interactions. This design dramatically reduces the hardware barrier, making powerful AI models accessible for local use on consumer-grade systems.
