About 1/64 of Ling-3.0-flash fires per token. 512 routed experts plus one shared, 8 activated, out of 124B total for 5.1B active. inclusionAI, which is Ant Group’s lab, put the weights up Aug 4 under MIT. Their card claims it matches or beats Ring-2.6-1T, their own trillion-param flagship, at roughly 12.4% of the total params and 8.1% of the active ones. Those are the lab’s own reported numbers so weight them accordingly, but the architecture is at least checkable: native hybrid linear attention adopted from the start of pretraining rather than retrofitted, with 35 KDA layers alternating 5:1 against 7 gated MLA layers. The reason I think this deserves a thread separate from the usual cost conversation is that it reframes what the cheap tier even means. If a lab can cut activated params by 12x and hold its own benchmark line, then a cheap model isn’t a degraded version of a big one. It’s the same capability with fewer experts firing, and the big model is paying for parameters it was never going to use on that token. Before anyone gets excited about running it: no GGUF and no llama.cpp support at release. Their serving examples use 4 GPUs on their own SGLang and vLLM forks. You still have to hold 124B weights somewhere, so it’s cheap to run, not cheap to own. Is the activated-param count actually the number that matters now, or is total VRAM still the only cost anyone outside a datacenter ever feels? submitted by /u/jkris050
Originally posted by u/jkris050 on r/ArtificialInteligence