Open weights are the cheapest part of running an open model

Nathan Lambert updated his open models reading list on September 11, and what it maps is mostly a fight about permission. He puts the gap between open and closed models at roughly four to six months and notes every leading open model since 2024 has come from a Chinese lab. He calls distillation, training on another model’s output tokens, the single most eventful debate around open models this year, and cites a paper showing reasoning traces could be systematically extracted from proprietary APIs, a technique he says Anthropic confirmed Chinese labs used. The same week, the radar carried Garry Tan arguing American open-weight labs should distill frontier models too, and a thread on r/LocalLLaMA titled Right to Intelligence.

So the argument is about who may copy whom, and whether weights should be publishable at all. Both are real arguments.

The vault’s page on local stacks answers a different question, and it is the one you hit the day after you win the first one. A local stack is not a local model. It is weights, plus a serving runtime, plus an agent harness, plus acceptance checks. Local is not free either. It moves cost into hardware, power, cooling, maintenance, slower iteration, lower peak capability, and the time of whoever keeps it running. Quantization helps a model load, but KV cache memory still grows with context length and concurrency, which is why a model that loads fine can still fall over on a long agent run with tool fanout.

Concede the policy fight is upstream of everything. If publishing weights becomes illegal, none of the rest matters.

Still, for anyone deciding whether to actually run one of these, the licence is the cheapest input in the build. The weights are free. The stack has a bill.