Freetoken

Running large language models locally usually comes down to a hard hardware boundary: video RAM. If a model does not fit into your GPU’s VRAM, performance usually falls off a cliff as soon as standard runtimes fall back to system memory over the PCIe bus. Mixture of Experts (MoE) architectures offer an attractive theoretical escape hatch. While the total parameter count can be large (30B to 70B+), only a sparse subset of expert layers activates for any given token. However, standard local runtimes still require loading the entire weight footprint into memory, which puts 30B+ models out of reach for everyday consumer laptops with 8 GB of VRAM. ...